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https://github.com/jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress

Papers on Graph neural network(GNN)
https://github.com/jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress

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Papers on Graph neural network(GNN)

Host: GitHub
URL: https://github.com/jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress
Owner: jdlc105
Created: 2018-12-11T06:47:38.000Z (almost 6 years ago)
Default Branch: master
Last Pushed: 2023-09-18T22:09:04.000Z (about 1 year ago)
Last Synced: 2024-08-01T16:51:10.603Z (3 months ago)
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Forks: 107
Open Issues: 3
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

awesome-machine-learning-resources - **[List - read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress?style=social) (Table of Contents)

README

# Must-read papers and continuous track on Graph Neural Network (GNN) progress

Many important real-world applications and issues come in the form of graphs, such as social network, protein-protein interaction network, brain network, chemical molecular graph and 3D point cloud. Therefore, driven by the above interdisciplinary research, the neural network model for graph data has become an emerging research hotspot. ***GNN and its variants are an emerging and powerful neural network model. Its applications are no longer limited to the original field of social network. It has flourished in many other areas, such as Data Visualization, Image Processing, NLP, Recommendation System, Computer Vision, Bioinformatics, Chemical informatics, Drug Development and Discovery, Smart Transportation.*** This project focuses on GNN, which lists relevant must-read papers and keeps track of progress. Note that actual overall progress in GNN should include, but not be limited to, these papers. We look forward to promoting this direction and providing several helps to researchers in this direction.

Contributed by Allen Bluce (Bentian Li) and Anne Bluce (Yunxia Lin), If there is something wrong or GNN-related issue, welcome to send email (Address: [email protected], [email protected]).

***Technology Keyword: Graph Neural Network, Graph convolutional network, Graph network, Graph attention network, Graph auto-encoder, Graph convolutional reinforcement learning, Graph capsule neural network....***

```diff
+ Very hot research topic:
```
***The most representative work--Semi-supervised classification with graph convolutional networks (GCNs) proposed by T.N. Kipf and M. Welling (ICLR2017 [5] in conference paper list) has been cited 1,020 times in Google Scholar (on 09 May 2019).*** Update: 1, 065 times (on 20 May 2019); Update: 1, 106 times (on 27 May 2019); Update: 1, 227 times (on 19 June 2019); Update: 1, 377 times (on 8 July 2019); Update: 1, 678 times (on 17 Sept. 2019); Update: 1, 944 times (on 29 Oct. 2019); Update: 2, 232 times (on 9 Dec. 2019); Update: 2, 677 times (on 2 Feb. 2020).Update: 3, 018 times (on 17 March. 2020); Update: 3,560 times (on 27 May. 2020); Update: 4,060 times (on 3 July. 2020); Update: 5,371 times (on 25 Oct. 2020). Update: 6,258 times (on 01 Jan. 2021). Update: 6,672 times (on 07 Feb. 2021). Update: 8,454 times (on 16 June. 2021). Update: 14,251 times (on 21 April. 2022). **Update: 22,270 times (on 28 March 2023)**.

***Project Start time: 11 Dec 2018, Latest updated time: 28 March 2023***. Thanks for giving us so many stars and supports from the developers and scientists on Github around the world！！！ We will continue to make this project better.

```diff
+ News: Recent Papers about GNN models and their applications have come from ICML2022, KDD2022,... We are waiting for more paper to be released.
```

## ![#f03c15](https://via.placeholder.com/15/f03c15/000000?text=+) `Survey papers`:

1. Bronstein M M, Bruna J, LeCun Y, et al. **Geometric deep learning: going beyond euclidean data**. IEEE Signal Processing Magazine, 2017, 34(4): 18-42. [paper](https://ieeexplore.ieee.org/abstract/document/7974879/)

2. Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun, **Graph Neural Networks: A Review of Methods and Applications**, ArXiv, 2018. [paper](https://arxiv.org/pdf/1812.08434.pdf).

3. Battaglia P W, Hamrick J B, Bapst V, et al. **Relational inductive biases, deep learning, and graph networks**, arXiv 2018. [paper](https://arxiv.org/pdf/1806.01261.pdf)

4. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu(Fellow,IEEE), **A Comprehensive Survey on Graph Neural Networks**, IEEE Transactions on Neural Networks and Learning Systems, 2020. [paper](https://ieeexplore.ieee.org/abstract/document/9046288).

5. Ziwei Zhang, Peng Cui, Wenwu Zhu, **Deep Learning on Graphs: A Survey**, IEEE Transactions on Knowledge and Data Engineering, 2020. [paper](https://ieeexplore.ieee.org/abstract/document/9039675).

6. Chen Z, Chen F, Zhang L, et al. **Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks**. arXiv preprint. 2020. [paper](https://arxiv.org/pdf/2002.11867.pdf)

7. Abadal S, Jain A, Guirado R, et al. **Computing Graph Neural Networks: A Survey from Algorithms to Accelerators**. arXiv preprint. 2020. [paper](https://arxiv.org/abs/2010.00130)

8. Lamb L, Garcez A, Gori M, et al. **Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective**. arXiv preprint. 2020. [paper](https://arxiv.org/pdf/2003.00330.pdf)

9. **Computing graph neural networks: A survey from algorithms to accelerators**. ACM Computing Surveys, 2021. [paper](https://dl-acm-org.ilyvt.top/doi/pdf/10.1145/3477141)

9. **Survey on Graph Neural Network Acceleration: An Algorithmic Perspective**. IJCAI 2022. [paper](https://www.ijcai.org/proceedings/2022/0772.pdf)

9. **Graph neural networks in recommender systems: a survey**. ACM Computing Surveys, 2022. [paper](https://dl-acm-org.ilyvt.top/doi/pdf/10.1145/3535101)

9. **Trustworthy graph neural networks: Aspects, methods and trends**. arXiv preprint, 2022. [paper](https://arxiv.org/pdf/2205.07424.pdf)

9. **Explainability in Graph Neural Networks: A Taxonomic Survey**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. [paper](https://ieeexplore.m7h.net/abstract/document/9875989)

9. **Graph Lifelong Learning: A Survey**. IEEE Computational Intelligence Magazine, 2023. [paper](https://ieeexplore.m7h.net/abstract/document/10026151)

9. **A Comprehensive Survey of Graph-level Learning**. arXiv preprint, 2023. [paper](https://arxiv.org/pdf/2301.05860.pdf)

9. **Self-Supervised Learning of Graph Neural Networks: A Unified Review**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. [paper](https://ieeexplore.m7h.net/document/9764632)

## ![#f03c15](https://via.placeholder.com/15/f03c15/000000?text=+) `Journal papers`:

1. F. Scarselli, M. Gori, A.C. Tsoi, M. Hagenbuchner, G. Monfardini, **The graph neural network model**, IEEE Transactions on Neural Networks(IEEE Transactions on Neural Networks and Learning Systems), 2009. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4700287&tag=1).

2. Scarselli F, Gori M, Tsoi A C, et al. **Computational capabilities of graph neural networks**, IEEE Transactions on Neural Networks, 2009. [paper](http://xueshu.baidu.com/s?wd=paperuri%3A%28805e5d5918a5146f2869293957ff0613%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fieeexplore.ieee.org%2Fdocument%2F4703190%2F&ie=utf-8&sc_us=16054091727677360492).

3. Micheli A . **Neural Network for Graphs: A Contextual Constructive Approach**. IEEE Transactions on Neural Networks, 2009. [paper](https://ieeexplore.ieee.org/document/4773279).

4. Goles, Eric, and Gonzalo A. Ruz. **Dynamics of Neural Networks over Undirected Graphs**. Neural Networks, 2015. [paper](https://www.sciencedirect.com/science/article/pii/S0893608014002688).

5. Z. Luo, L. Liu, J. Yin, Y. Li, Z. Wu, **Deep Learning of Graphs with Ngram Convolutional Neural Networks**, IEEE Transactions on Knowledge & Data Engineering, 2017. [paper](https://ieeexplore.ieee.org/document/7961216). [code](https://github.com/zhilingluo/NgramCNN).

6. Petroski Such F , Sah S , Dominguez M A , et al. **Robust Spatial Filtering with Graph Convolutional Neural Networks**. IEEE Journal of Selected Topics in Signal Processing, 2017. [paper](https://ieeexplore.ieee.org/document/7979525).

7. Kawahara J, Brown C J, Miller S P, et al. **BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment**. NeuroImage, 2017. [paper](https://www.sciencedirect.com/science/article/pii/S1053811916305237).

8. Muscoloni A , Thomas J M , Ciucci S , et al. **Machine learning meets complex networks via coalescent embedding in the hyperbolic space**. Nature Communications, 2017. [paper](https://www.nature.com/articles/s41467-017-01825-5).

9. D.M. Camacho, K.M. Collins, R.K. Powers, J.C. Costello, J.J. Collins, **Next-Generation Machine Learning for Biological Networks**, Cell, 2018. [paper](https://www.cell.com/cell/fulltext/S0092-8674(18)30592-0?rss=yes).

10. Marinka Z , Monica A , Jure L . **Modeling polypharmacy side effects with graph convolutional networks**. Bioinformatics, 2018. [paper](https://cs.stanford.edu/people/jure/pubs/drugcomb-ismb18.pdf).

11. Sarah P , Ira K S , Enzo F , et al. **Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer’s Disease**. Medical Image Analysis, 2018. [paper](https://www.sciencedirect.com/science/article/pii/S1361841518303554).

12. Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante, Martin Rajchl, Matthew Lee, Ben Glocker, Daniel Rueckert, **Metric learning with spectral graph convolutions on brain connectivity networks**, NeuroImage, 2018. [paper](https://www.sciencedirect.com/science/article/pii/S1053811917310765).

13. Xie T , Grossman J C . **Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties**. Physical Review Letters, 2018. [paper](http://dspace.mit.edu/bitstream/handle/1721.1/114632/PhysRevLett.120.145301.pdf;jsessionid=21377E3B0A383EDFAF9C4C0C9D9A05A0?sequence=1).

14. Phan, Anh Viet, Minh Le Nguyen, Yen Lam Hoang Nguyen, and Lam Thu Bui. **DGCNN: A Convolutional Neural Network over Large-Scale Labeled Graphs**. Neural Networks, 2018. [paper](https://www.sciencedirect.com/science/article/pii/S0893608018302636)

15. Song T, Zheng W, Song P, et al. **Eeg emotion recognition using dynamical graph convolutional neural networks**. IEEE Transactions on Affective Computing, 2018. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8320798)

16. Levie R, Monti F, Bresson X, et al. **Cayleynets: Graph convolutional neural networks with complex rational spectral filters**. IEEE Transactions on Signal Processing 2019. [paper](https://ieeexplore.ieee.org/document/8521593)

17. Zhang, Zhihong, Dongdong Chen, Jianjia Wang, Lu Bai, and Edwin R. Hancock. **Quantum-Based Subgraph Convolutional Neural Networks.** Pattern Recognition, 2019. [paper](https://www.sciencedirect.com/science/article/pii/S0031320318303820)

18. Qin A, Shang Z, Tian J, et al. **Spectral–Spatial Graph Convolutional Networks for Semisupervised Hyperspectral Image Classification**. IEEE Geoscience and Remote Sensing Letters, 2019. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8474300)

19. Coley C W, Jin W, Rogers L, et al. **A graph-convolutional neural network model for the prediction of chemical reactivity**. Chemical Science, 2019. [paper](https://pubs.rsc.org/en/content/articlehtml/2019/sc/c8sc04228d)

20. Zhang Z, Chen D, Wang Z, et al. **Depth-based Subgraph Convolutional Auto-Encoder for Network Representation Learning**. Pattern Recognition, 2019. [paper](https://www.sciencedirect.com/science/article/pii/S0031320319300354)

21. Hong Y, Kim J, Chen G, et al. **Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks**. IEEE transactions on medical imaging, 2019. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8691605)

22. Khodayar M, Mohammadi S, Khodayar M E, et al. **Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-temporal Solar Irradiance Forecasting**. IEEE Transactions on Sustainable Energy, 2019. [paper](https://ieeexplore.ieee.org/abstract/document/8663347)

23. Zhang Q, Chang J, Meng G, et al. **Learning graph structure via graph convolutional networks**. Pattern Recognition, 2019. [paper](https://www.sciencedirect.com/science/article/pii/S0031320319302432)

24. Xuan P, Pan S, Zhang T, et al. **Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations**. Cells, 2019. [paper](https://www_mdpi.gg363.site/2073-4409/8/9/1012)

25. Sun M, Zhao S, Gilvary C, et al. **Graph convolutional networks for computational drug development and discovery**. Briefings in bioinformatics, 2019. [paper](https://academic_oup.gg363.site/)

26. Spier N, Nekolla S, Rupprecht C, et al. **Classification of Polar Maps from Cardiac Perfusion Imaging with Graph-Convolutional Neural Networks**. Scientific reports, 2019. [paper](https://www_nature.gg363.site/articles/s41598-019-43951-8)

27. Heyuan Shi, et al. **Hypergraph-Induced Convolutional Networks for Visual Classification**. IEEE Transactions on Neural Networks and Learning Systems, 2019. [paper](https://ieeexplore_ieee.gg363.site/abstract/document/8478794)

28. S.Pan, et al. **Learning Graph Embedding With Adversarial Training Methods**. IEEE Transactions on Cybernetics, 2019. [paper](https://ieeexplore.ieee.org/document/8822591)

29. D. Grattarola, et al. **Change Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds**. IEEE Transactions on Neural Networks and Learning Systems. 2019. [paper](https://ieeexplore.ieee.org/document/8782142)

30. Kan Guo, et al. **Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction**. IEEE Transactions on Intelligent Transportation Systems. 2020. [paper](https://ieeexplore.ieee.org/document/8959420)

31. Ruiz L, et al. **Invariance-preserving localized activation functions for graph neural networks**. IEEE Transactions on Signal Processing, 2020. [paper](https://ieeexplore.ieee.org/document/8911416)

32. Li J, et al. **Neural Inductive Matrix Completion with Graph Convolutional Networks for miRNA-disease Association Prediction**. Bioinformatics, 2020. [paper](https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btz965/5697092)

33. Bingzhi Chen, et al. **Label Co-occurrence Learning with Graph Convolutional Networks for Multi-label Chest X-ray Image Classification**. IEEE Journal of Biomedical and Health Informatics, 2020. [paper](https://ieeexplore.ieee.org/document/8961143)

34. Kunjin Chen, et al. **Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks**. IEEE Journal on Selected Areas in Communications, 2020. [paper](https://ieeexplore.ieee.org/document/8892483)

35. Manessi, Franco, et al. **Dynamic graph convolutional networks**. Pattern Recognition, 2020. [paper](https://sciencedirect.xilesou.top/science/article/pii/S0031320319303036)

36. Jiang X, Zhu R, Li S, et al. **Co-embedding of Nodes and Edges with Graph Neural Networks**. IEEE Transactions on Pattern Analysis and Machine Intelligence. [paper](https://pubmed.ncbi.nlm.nih.gov/33052851/)

37. Wang Z, Ji S. **Second-order pooling for graph neural networks**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020. [paper](https://ieeexplore.ieee.org/abstract/document/9104936)

38. Indro Spinelli, et al. **Adaptive Propagation Graph Convolutional Network**. IEEE Transactions on Neural Networks and Learning Systems, 2020. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9206045)

39. Zhou Fan, et al. **Reinforced Spatiotemporal Attentive Graph Neural Networks for Traffic Forecasting**. IEEE Internet of Things Journal, 2020. [paper](https://ieeexplore-ieee-org.flyyouth.top/abstract/document/9003261)

40. Wang S H, et al. **Covid-19 classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network.** Information Fusion, 2020. [paper](https://www-sciencedirect-com.flyyouth.top/science/article/pii/S1566253520303705)

41. Ruiz, Luana et al. **Gated Graph Recurrent Neural Networks**, IEEE Transactions on Signal Processing. 2020. [paper](https://arxiv.org/abs/2002.01038)

42. Gama, Fernando et al. **Stability Properties of Graph Neural Networks**, IEEE Transactions on Signal Processing. 2020. [paper](https://arxiv.org/abs/1905.04497)

43. He, Xin et al. **MV-GNN: Multi-View Graph Neural Network for Compression Artifacts Reduction**, IEEE Transactions on Image Processing. 2020. [paper](https://ieeexplore-ieee-org.flyyouth.top/abstract/document/9096540/)

44. Holzinger A, et al. **Towards multi-modal causability with Graph Neural Networks enabling information fusion for explainable AI**, Information Fusion, 2021. [paper](https://www-sciencedirect-com.cam.80599.net/science/article/pii/S1566253521000142)

45. Bianchi F M, et al. **Graph neural networks with convolutional arma filters**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. [paper](https://ieeexplore.7648.top/abstract/document/9336270/)

46. Bentian Li, et al. **Dual Mutual Robust Graph Convolutional Network for Weakly Supervised Node Classification in Social Networks of Internet of People**. IEEE Internet of Things Journal, 2021. [paper](https://ieeexplore.ieee.org/document/9463468)

47. Chowdhury A, et al. **Unfolding wmmse using graph neural networks for efficient power allocation**. IEEE Transactions on Wireless Communications, 2021. [paper](https://ieeexplore.7648.top/abstract/document/9403959/)

### ![#1589F0](https://via.placeholder.com/15/1589F0/000000?text=+) `Progress in 2022 Journal Papers`:
----------------------------------------------------------------------------------------------------------------------------------------------------------
**Novel GNN methods proposed in 2022**

48. **Deep Constraint-Based Propagation in Graph Neural Networks**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9405452)

50. **Learning Deep Graph Representations via Convolutional Neural Networks**. IEEE Transactions on Knowledge and Data Engineering, 2022. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9158338)

51. **On Inductive–Transductive Learning With Graph Neural Networks**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9335498)

---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
**Novel GNN-based applications proposed in 2022**

51. **A Graph Neural Network-Based Digital Twin for Network Slicing Management**. IEEE Transactions on Industrial Informatics, 2022. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9310275)

53. **Low-Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks**. IEEE Internet of Things Journal, 2022. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9446513)

53. **Resilient UAV Swarm Communications With Graph Convolutional Neural Network**. IEEE Journal on Selected Areas in Communications, 2022. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9618911)

53. **A Graph Neural Network Framework for Social Recommendations**. IEEE Transactions on Knowledge and Data Engineering, 2022. [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9139346)

### ![#1589F0](https://via.placeholder.com/15/1589F0/000000?text=+) `Progress in 2023 Journal Papers`:
----------------------------------------------------------------------------------------------------------------------------------------------------------
**Novel GNN methods proposed in 2023**

55. **Higher-Order Interaction Goes Neural: A Substructure Assembling Graph Attention Network for Graph Classification**. IEEE Transactions on Knowledge and Data Engineering, 2023. [paper](https://ieeexplore.m7h.net/stamp/stamp.jsp?tp=&arnumber=9516965)

53. **Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks**. IEEE Transactions on Knowledge and Data Engineering, 2023. [paper](https://ieeexplore.m7h.net/stamp/stamp.jsp?tp=&arnumber=9415142)

53. **Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. [paper](https://ieeexplore.m7h.net/document/9721082)

53. **Neighbor-Anchoring Adversarial Graph Neural Networks**. IEEE Transactions on Knowledge and Data Engineering, 2023. [paper](https://ieeexplore.m7h.net/document/9453132)

53. **HGNAS++: Efficient Architecture Search for Heterogeneous Graph Neural Networks**. IEEE Transactions on Knowledge and Data Engineering, 2023. [paper](https://ieeexplore.m7h.net/stamp/stamp.jsp?tp=&arnumber=10040227)

53. **Multi-View Tensor Graph Neural Networks Through Reinforced Aggregation**. IEEE Transactions on Knowledge and Data Engineering, 2023. [paper](https://ieeexplore.m7h.net/document/9711926)

53. **Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. [paper](https://ieeexplore.m7h.net/document/9773017/)

53. **Reinforced Causal Explainer for Graph Neural Networks**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. [paper](https://ieeexplore.m7h.net/document/9763330)

63. **Combining Graph Neural Networks With Expert Knowledge for Smart Contract Vulnerability Detection**. IEEE Transactions on Knowledge and Data Engineering, 2023. [paper](https://ieeexplore.m7h.net/stamp/stamp.jsp?tp=&arnumber=9477066)

53. **Bundle Recommendation and Generation With Graph Neural Networks**. IEEE Transactions on Knowledge and Data Engineering, 2023. [paper](https://ieeexplore.m7h.net/document/9546546)

53. **Integrating Multi-Label Contrastive Learning With Dual Adversarial Graph Neural Networks for Cross-Modal Retrieval**. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. [paper](https://ieeexplore.m7h.net/document/9815553)

## ![#f03c15](https://via.placeholder.com/15/f03c15/000000?text=+) `Conference papers`:

1. Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, et al. **Convolutional networks on graphs for learning molecular fingerprints**, NeurIPS(NIPS) 2015. [paper](http://papers.nips.cc/paper/5954-convolutional-networks-on-graphs-for-learning-molecular-fingerprints.pdf). [code](https://github.com/HIPS/neural-fingerprint).

2. M. Niepert, M. Ahmed, K. Kutzkov, **Learning Convolutional Neural Networks for Graphs**, ICML 2016. [paper](http://proceedings.mlr.press/v48/niepert16.pdf).

3. S. Cao, W. Lu, Q. Xu, **Deep neural networks for learning graph representations**, AAAI 2016. [paper](https://aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12423).

4. M. Defferrard, X. Bresson, P. Vandergheynst, **Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering**, NeurIPS(NIPS) 2016. [paper](http://papers.nips.cc/paper/6081-convolutional-neural-networks-on-graphs-with-fast-localized-spectral-filtering.pdf). [code](https://github.com/mdeff/cnn_graph).

5. T.N. Kipf, M. Welling, **Semi-Supervised Classification with Graph Convolutional Networks**, ICLR 2017. [paper](https://www.ics.uci.edu/~welling/publications/papers/Submitted2016-SSL-GCNN.pdf). [code](http://tkipf.github.io/graph-convolutional-networks/).

6. A. Fout, B. Shariat, J. Byrd, A. Benhur, **Protein Interface Prediction using Graph Convolutional Networks**, NeurIPS(NIPS) 2017. [paper](http://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks).

7. Monti F, Bronstein M, Bresson X. **Geometric matrix completion with recurrent multi-graph neural networks**, NeurIPS(NIPS) 2017. [paper](http://papers.nips.cc/paper/6960-geometric-matrix-completion-with-recurrent-multi-graph-neural-networks.pdf).

8. Simonovsky M, Komodakis N. **Dynamic edgeconditioned filters in convolutional neural networks on graphs**, CVPR. 2017. [paper](http://openaccess.thecvf.com/content_cvpr_2017/papers/Simonovsky_Dynamic_Edge-Conditioned_Filters_CVPR_2017_paper.pdf)

9. R. Li, S. Wang, F. Zhu, J. Huang, **Adaptive Graph Convolutional Neural Networks**, AAAI 2018. [paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16642)

10. J. You, B. Liu, R. Ying, V. Pande, J. Leskovec, **Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation**, NeurIPS(NIPS) 2018. [paper](http://xueshu.baidu.com/s?wd=paperuri%3A%28dd21a587e909dfc3a0159ca428d3580a%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Farxiv.org%2Fpdf%2F1806.02473&ie=utf-8&sc_us=3351961245348982134&sc_as_para=sc_lib%3A).

11. C. Zhuang, Q. Ma, **Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification**, WWW 2018. [paper](https://dl.acm.org/citation.cfm?doid=3178876.3186116)

12. H. Gao, Z. Wang, S. Ji, **Large-Scale Learnable Graph Convolutional Networks**, KDD 2018. [paper](https://dl.acm.org/citation.cfm?doid=3219819.3219947)

13. D. Zügner, A. Akbarnejad, S. Günnemann, **Adversarial Attacks on Neural Networks for Graph Data**, KDD 2018. [paper](https://dl.acm.org/citation.cfm?doid=3219819.3220078)

14. Ying R , He R , Chen K , et al. **Graph Convolutional Neural Networks for Web-Scale Recommender Systems**. KDD 2018. [paper](https://cs.stanford.edu/people/jure/pubs/pinsage-kdd18.pdf)

15. P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, Y. Bengio, **Graph Attention Networks**, ICLR, 2018. [paper](https://arxiv.org/pdf/1710.10903.pdf)

16. Beck, Daniel Edward Robert, Gholamreza Haffari and Trevor Cohn. **Graph-to-Sequence Learning using Gated Graph Neural Networks.** ACL 2018. [paper](https://arxiv.org/pdf/1806.09835.pdf)

17. Yu B, Yin H, Zhu Z. **Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting**. IJCAI 2018. [paper](https://arxiv.xilesou.top/pdf/1709.04875.pdf)

18. Chen J , Zhu J , Song L . **Stochastic Training of Graph Convolutional Networks with Variance Reduction**. ICML 2018. [paper](http://proceedings.mlr.press/v80/chen18p.html)

19. Gusi Te, Wei Hu, Amin Zheng, Zongming Guo, **RGCNN: Regularized Graph CNN for Point Cloud Segmentation**. ACM Multimedia 2018. [paper](http://www.icst.pku.edu.cn/F/intro/huwei/index_files/papers/ACMMM18_Te.pdf), [code](https://github.com/tegusi/RGCNN),

20. Talukdar, Partha, Shikhar Vashishth, Shib Sankar Dasgupta and Swayambhu Nath Ray. **Dating Documents using Graph Convolution Networks.** ACL 2018. [paper](http://anthology.aclweb.org/attachments/P/P18/P18-1149.Poster.pdf), [code](http://github.com/malllabiisc/neuraldater)

21. Sanchez-Gonzalez A , Heess N , Springenberg J T , et al. **Graph networks as learnable physics engines for inference and control**. ICML 2018. [paper](http://proceedings.mlr.press/v80/sanchez-gonzalez18a/sanchez-gonzalez18a.pdf)

22. Muhan Zhang, Yixin Chen. **Link Prediction Based on Graph Neural Networks**. NeurIPS(NIPS) 2018. [paper](https://arxiv.org/pdf/1802.09691.pdf)

23. Chen, Jie, Tengfei Ma, and Cao Xiao. **FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling.** ICLR 2018. [paper](https://arxiv.org/pdf/1801.10247.pdf)

24. Zhang, Zhen, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, and Can Wang. **ANRL: Attributed Network Representation Learning via Deep Neural Networks.**. IJCAI 2018. [paper](https://www.ijcai.org/proceedings/2018/438)

25. Rahimi A , Cohn T , Baldwin T . **Semi-supervised User Geolocation via Graph Convolutional Networks.** ACL 2018. [paper](https://arxiv.org/pdf/1804.08049.pdf)

26. Morris C , Ritzert M , Fey M , et al.**Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks.**. AAAI 2019. [paper](https://arxiv.org/abs/1810.02244v1)

27. Xu K, Hu W, Leskovec J, et al. **How Powerful are Graph Neural Networks?**, ICLR 2019. [paper](https://arxiv.org/pdf/1810.00826.pdf)

28. Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann. **Combining Neural Networks with Personalized PageRank for Classification on Graphs**, ICLR 2019. [paper](https://arxiv.org/pdf/1810.05997.pdf)

29. Daniel Zügner, Stephan Günnemann. **Adversarial Attacks on Graph Neural Networks via Meta Learning**, ICLR 2019. [paper](https://openreview.net/pdf?id=Bylnx209YX)

30. Zhang Xinyi, Lihui Chen. **Capsule Graph Neural Network**, ICLR 2019. [paper](https://openreview.net/pdf?id=Byl8BnRcYm)

31. Liao, R., Zhao, Z., Urtasun, R., and Zemel, R. **LanczosNet: Multi-Scale Deep Graph Convolutional Networks**, ICLR 2019, [paper](https://arxiv.org/abs/1901.01484)

32. Bingbing Xu, Huawei Shen, Qi Cao, Yunqi Qiu, Xueqi Cheng. **Graph Wavelet Neural Network**, ICLR 2019, [paper](https://openreview.net/forum?id=H1ewdiR5tQ)

33. Hu J, Guo C, Yang B, et al. **Stochastic Weight Completion for Road Networks using Graph Convolutional Networks** ICDE. 2019. [paper](https://openreview.net/forum?id=H1ewdiR5tQ)

34. Yao L, Mao C, Luo Y . **Graph Convolutional Networks for Text Classification**. AAAI 2019. [paper](https://arxiv.org/pdf/1809.05679.pdf)

35. Landrieu L , Boussaha M . **Point Cloud Oversegmentation with Graph-Structured Deep Metric Learning**. CVPR 2019. [paper](https://arxiv.org/abs/1904.02113)

36. Si C , Chen W , Wang W , et al. **An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition**. CVPR 2019. [paper](https://arxiv.org/abs/1902.09130)

37. Cucurull G , Taslakian P , Vazquez D . **Context-Aware Visual Compatibility Prediction**. CVPR 2019. [paper](https://arxiv.org/pdf/1902.03646.pdf)

38. Jia-Xing Zhong, Nannan Li, Weijie Kong, Shan Liu, Thomas H. Li, Ge Li. **Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection**. CVPR 2019. [paper](https://arxiv.org/abs/1903.07256)

39. Michael Kampffmeyer, Yinbo Chen, Xiaodan Liang, Hao Wang, Yujia Zhang, Eric P. Xing. **Rethinking Knowledge Graph Propagation for Zero-Shot Learning**. CVPR 2019. [paper](https://arxiv.org/abs/1805.11724v3)

40. Arushi Goel, Keng Teck Ma, Cheston Tan. **An End-to-End Network for Generating Social Relationship Graphs**. CVPR 2019. [paper](https://arxiv.org/abs/1903.09784)

41. Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, Xiaokang Yang. **Learning Context Graph for Person Search**. CVPR 2019 [paper](https://arxiv.org/pdf/1904.01830.pdf)

42. Zhongdao Wang, Liang Zheng, Yali Li, Shengjin Wang. **Linkage Based Face Clustering via Graph Convolution Network**. CVPR 2019 [paper](https://arxiv.org/abs/1903.11306)

43. Lei Yang, Xiaohang Zhan, Dapeng Chen, Junjie Yan, Chen Change Loy, Dahua Lin. **Learning to Cluster Faces on an Affinity Graph**. CVPR 2019 [paper](https://arxiv.org/pdf/1904.02749.pdf)

44. Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang. **Graph Convolutional Networks with EigenPooling**. KDD2019, [paper](https://arxiv.org/pdf/1904.13107.pdf)

45. Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin. **Graph Neural Networks for Social Recommendation**. WWW2019, [paper](https://arxiv.org/pdf/1902.07243.pdf)

46. Kim J, Kim T, Kim S, et al. **Edge-labeling Graph Neural Network for Few-shot Learning**. CVPR 2019. [paper](https://arxiv.org/pdf/1905.01436.pdf)

47. Jessica V. Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson. **INFERRING JAVASCRIPT TYPES USING GRAPH NEURAL NETWORKS**. ICLR 2019. [paper](https://arxiv.org/pdf/1905.06707.pdf)

48. Emanuele Rossi, Federico Monti, Michael Bronstein, Pietro liò. **ncRNA Classification with Graph Convolutional Networks**. SIGKDD 2019. [paper](https://arxiv.org/pdf/1905.06515.pdf)

49. Wu F, Zhang T, Souza Jr A H, et al. **Simplifying Graph Convolutional Networks**. ICML 2019. [paper](https://arxiv.org/abs/1902.07153).

50. Junhyun Lee, Inyeop Lee, Jaewoo Kang. **Self-Attention Graph Pooling**. ICML 2019. [paper](https://arxiv.org/pdf/1904.08082.pdf).

51. Chiang W L, Liu X, Si S, et al. **Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks.** SIGKDD 2019. [paper](https://arxiv.org/pdf/1905.07953.pdf).

52. Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos, **Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks**. SIGKDD 2019. [paper](https://arxiv.org/pdf/1905.08865.pdf).

53. Wu S, Tang Y, Zhu Y, et al. **Session-based Recommendation with Graph Neural Networks**. AAAI 2019. [paper](https://arxiv.org/pdf/1811.00855.pdf).

54. Qu M, Bengio Y, Tang J. **GMNN: Graph Markov Neural Networks**. ICML 2019. [paper](https://arxiv.org/abs/1905.06214)[coder](https://github.com/DeepGraphLearning/GMNN).

55. Li Y, Gu C, Dullien T, et al. **Graph Matching Networks for Learning the Similarity of Graph Structured Objects**, ICML 2019.[paper](https://arxiv.org/abs/1904.12787).

56. Gao H, Ji S. **Graph U-Nets**, ICML 2019. [paper](https://arxiv.org/abs/1905.05178).

57. Bojchevski A, Günnemann S. **Adversarial Attacks on Node Embeddings via Graph Poisoning**, ICML 2019. [paper](https://arxiv.org/pdf/1809.01093.pdf).

58. Jeong D, Kwon T, Kim Y, et al. **Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance**. ICML 2019. [paper](http://proceedings.mlr.press/v97/jeong19a/jeong19a.pdf).

59. Zhang G, He H, Katabi D. **Circuit-GNN: Graph Neural Networks for Distributed Circuit Design**. ICML 2019. [paper](http://proceedings.mlr.press/v97/zhang19e/zhang19e.pdf).

60. Alet F, Jeewajee A K, Bauza M, et al. **Graph Element Networks: adaptive, structured computation and memory**, ICML 2019. [paper](https://arxiv.org/abs/1904.09019).

61. Rieck B, Bock C, Borgwardt K. **A Persistent Weisfeiler-Lehman Procedure for Graph Classification**, ICML 2019. [paper](http://bastian.rieck.me/research/ICML2019_P-WL.pdf).

62. Walker I, Glocker B. **Graph Convolutional Gaussian Processes**，ICML 2019. [paper](https://arxiv.org/abs/1905.05739).

63. Yu Y, Chen J, Gao T, et al. **DAG-GNN: DAG Structure Learning with Graph Neural Networks**, ICML 2019. [paper](https://arxiv.org/pdf/1904.10098.pdf).

64. Zhijiang Guo, Yan Zhang and Wei Lu, **Attention Guided Graph Convolutional Networks for Relation Extraction** ACL 2019. [paper](http://www.statnlp.org/paper/2019/attention-guided-graph-convolutional-networks-relation-extraction.html). [coder](https://github.com/Cartus/AGGCN_TACRED).

65. Chang Li, Dan Goldwasser. **Encoding Social Information with Graph Convolutional Networks for Political Perspective Detection in News Media** ACL 2019. [paper](https://www.cs.purdue.edu/homes/dgoldwas//downloads/papers/LiG_acl_2019.pdf).

66. Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-seng Chua, Maosong Sun. **Graph Neural Networks with Generated Parameters for Relation Extraction** ACL 2019. [paper](https://arxiv.org/abs/1902.00756).

67. Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, Partha Talukdar. **Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks** ACL 2019. [paper](https://arxiv.org/abs/1809.04283).

68. Cui Z, Li Z, Wu S, et al. **Dressing as a Whole: Outfit Compatibility Learning Based on Node-wise Graph Neural Networks** WWW 2019. [paper](https://dl.acm.org/citation.cfm?doid=3308558.3313444).

69. Zhang, Chris, et al. **Graph HyperNetworks for Neural Architecture Search.** ICLR 2019. [paper](https://openreview.net/pdf?id=rkgW0oA9FX).

70. Chen, Zhengdao, et al. **Supervised Community Detection with Line Graph Neural Networks.** ICLR 2019. [paper](https://openreview.net/pdf?id=H1g0Z3A9Fm).

71. Maron, Haggai, et al. **Invariant and Equivariant Graph Networks.** ICLR 2019. [paper](https://openreview.net/pdf?id=Syx72jC9tm).

72. Gulcehre, Caglar, et al. **Hyperbolic Attention Networks.** ICLR, 2019. [paper](https://openreview.net/pdf?id=rJxHsjRqFQ).

73. Prates, Marcelo O. R., et al. **Learning to Solve NP-Complete Problems -- A Graph Neural Network for the Decision TSP.** AAAI, 2019. [paper](https://arxiv.org/pdf/1809.02721.pdf).

74. Liu, Ziqi, et al. **GeniePath: Graph Neural Networks with Adaptive Receptive Paths.** AAAI, 2019. [paper](https://arxiv.org/pdf/1802.00910.pdf).

75. Keriven N, Peyré G. **Universal invariant and equivariant graph neural networks**. NeurIPS, 2019. [paper](https://arxiv.gg363.site/abs/1905.04943).

76. Qi Liu, et al. **Hyperbolic Graph Neural Networks**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/9033-hyperbolic-graph-neural-networks.pdf).

77. Zhitao Ying, et al. **GNNExplainer: Generating Explanations for Graph Neural Networks**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/9123-gnnexplainer-generating-explanations-for-graph-neural-networks.pdf).

78. Yaqin Zhou, et al. **Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/9209-devign-effective-vulnerability-identification-by-learning-comprehensive-program-semantics-via-graph-neural-networks.pdf).

79. Ehsan Hajiramezanali, et al. **Variational Graph Recurrent Neural Networks**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/9254-variational-graph-recurrent-neural-networks.pdf).

80. Sitao Luan, et al. **Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/9276-break-the-ceiling-stronger-multi-scale-deep-graph-convolutional-networks.pdf).

81. Difan Zou, et al. **Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/9303-layer-dependent-importance-sampling-for-training-deep-and-large-graph-convolutional-networks.pdf).

82. Seongjun Yun, et al. **Graph Transformer Networks**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/9367-graph-transformer-networks.pdf).

83. Andrei Nicolicioiu, et al. **Recurrent Space-time Graph Neural Networks**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/9444-recurrent-space-time-graph-neural-networks.pdf).

84. Nima Dehmamy, et al. **Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/9675-understanding-the-representation-power-of-graph-neural-networks-in-learning-graph-topology.pdf).

85. Maxime Gasse, et al. **Exact Combinatorial Optimization with Graph Convolutional Neural Networks**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/9690-exact-combinatorial-optimization-with-graph-convolutional-neural-networks.pdf).

86. Zhengdao Chen, et al. **On the equivalence between graph isomorphism testing and function approximation with GNNs**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/9718-on-the-equivalence-between-graph-isomorphism-testing-and-function-approximation-with-gnns.pdf).

87. Vineet Kosaraju, et al. **Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/8308-social-bigat-multimodal-trajectory-forecasting-using-bicycle-gan-and-graph-attention-networks.pdf).

88. Carl Yang, et al.**Conditional Structure Generation through Graph Variational Generative Adversarial Nets**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/8415-conditional-structure-generation-through-graph-variational-generative-adversarial-nets.pdf).

89. Naganand Yadati, et al.**HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/8430-hypergcn-a-new-method-for-training-graph-convolutional-networks-on-hypergraphs.pdf).

90. Haggai Maron, et al.**Provably Powerful Graph Networks**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/8488-provably-powerful-graph-networks.pdf).

91. Eliya Nachmani, et al.**Hyper-Graph-Network Decoders for Block Codes**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/8504-hyper-graph-network-decoders-for-block-codes.pdf).

92. Hanjun Dai, et al.**Learning Transferable Graph Exploration**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/8521-learning-transferable-graph-exploration.pdf).

93. Ryoma Sato, et al.**Approximation Ratios of Graph Neural Networks for Combinatorial Problems**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/8662-approximation-ratios-of-graph-neural-networks-for-combinatorial-problems.pdf).

94. Boris Knyazev, et al.**Understanding Attention and Generalization in Graph Neural Networks**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/8673-understanding-attention-and-generalization-in-graph-neural-networks.pdf).

95. Renjie Liao, et al.**Efficient Graph Generation with Graph Recurrent Attention Networks**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/8678-efficient-graph-generation-with-graph-recurrent-attention-networks.pdf).

96. Bryan Wilder, et al.**End to end learning and optimization on graphs**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/8715-end-to-end-learning-and-optimization-on-graphs.pdf).

97. Simon Du, et al.**Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/8809-graph-neural-tangent-kernel-fusing-graph-neural-networks-with-graph-kernels.pdf).

98. W. O. K. Asiri Suranga Wijesinghe, et al. **DFNets: Spectral CNNs for Graphs with Feedback-looped Filters**. NeurIPS, 2019. [paper](http://papers.nips.cc/paper/8834-dfnets-spectral-cnns-for-graphs-with-feedback-looped-filters.pdf).

99. Dong Wook Shu, et al.**3D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutions**. ICCV 2019. [paper](https://arxiv.org/abs/1905.06292)

100. Yujun Cai, et al. **Exploiting Spatial-temporal Relationships for 3D Pose Estimation via Graph Convolutional Networks**. ICCV 2019. [paper](https://cse.buffalo.edu/~jsyuan/papers/2019/Exploiting_Spatial-temporal_Relationships_for_3D_Pose_Estimation_via_Graph_Convolutional_Networks.pdf)

101. Runhao Zeng, et al. **Graph Convolutional Networks for Temporal Action Localization**. ICCV 2019. [paper](https://arxiv.org/abs/1909.03252)

102. Yin Bi, et al. **Graph-Based Object Classification for Neuromorphic Vision Sensing**. ICCV 2019. [paper](https://arxiv.org/abs/1908.06648)

103.Tianshui Chen, et al. **Learning Semantic-Specific Graph Representation for Multi-Label Image Recognition**. ICCV 2019. [paper](https://arxiv.org/abs/1908.07325)

104. Linjie Li, et al. **Relation-Aware Graph Attention Network for Visual Question Answering**. ICCV 2019. [paper](https://arxiv.org/abs/1903.12314)

105. Jiwoong Park, et al. **Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning**. ICCV 2019. [paper](https://arxiv.org/abs/1908.02441)

106. Runzhong Wang, et al. **Learning Combinatorial Embedding Networks for Deep Graph Matching**. ICCV 2019. [paper](https://arxiv.org/abs/1904.00597)

107. Zhiqiang Tao, et al. **Adversarial Graph Embedding for Ensemble Clustering**. IJCAI 2019. [paper](https://www.ijcai.org/proceedings/2019/0494.pdf)

108. Xiaotong Zhang, et al. **Attributed Graph Clustering via Adaptive Graph Convolution**. IJCAI 2019. [paper](https://arxiv.org/abs/1906.01210)

109. Jianwen Jiang, et al. **Dynamic Hypergraph Neural Networks**. IJCAI 2019. [paper](https://www.ijcai.org/proceedings/2019/0366.pdf)

110. Hogun Park, et al. **Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks**. IJCAI 2019. [paper](https://www.ijcai.org/proceedings/2019/0447.pdf)

111. Hao Peng, et al. **Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks**. IJCAI 2019. [paper](https://arxiv.org/abs/1906.04580)

112. Chengfeng Xu, et al. **Graph Contextualized Self-Attention Network for Session-based Recommendation**. IJCAI 2019. [paper](https://www.ijcai.org/proceedings/2019/0547.pdf)

113. Ruiqing Xu, et al. **Graph Convolutional Network Hashing for Cross-Modal Retrieval**. IJCAI 2019. [paper](https://www.ijcai.org/proceedings/2019/0138.pdf)

114. Bingbing Xu, et al. **Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning**. IJCAI 2019. [paper](https://www.ijcai.org/proceedings/2019/0267.pdf)

115. Zonghan Wu, et al. **Graph WaveNet for Deep Spatial-Temporal Graph Modeling**. IJCAI 2019. [paper](https://www.ijcai.org/proceedings/2019/0264.pdf)

116. Fenyu Hu, et al. **Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification**. IJCAI 2019. [paper](https://arxiv.org/abs/1902.06667)

117. Li Zheng, et al. **AddGraph: Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN**. IJCAI 2019. [paper](https://www.ijcai.org/proceedings/2019/0614.pdf)

118. Liang Yang, et al. **Dual Self-Paced Graph Convolutional Network: Towards Reducing Attribute Distortions Induced by Topology**. IJCAI 2019. [paper](https://www.ijcai.org/proceedings/2019/0564.pdf)

119. Liang Yang, et al. **Masked Graph Convolutional Network**. IJCAI 2019. [paper](https://www.ijcai.org/proceedings/2019/0565.pdf)

120. Xiaofeng Xu, et al. **Learning Image-Specific Attributes by Hyperbolic Neighborhood Graph Propagation**. IJCAI 2019. [paper](https://www.ijcai.org/proceedings/2019/0554.pdf)

121. Li G, Müller M, Thabet A, et al. **Can GCNs Go as Deep as CNNs?**. ICCV 2019. [paper](https://arxiv.org/pdf/1904.03751.pdf).

122. Park C, Lee C, Bahng H, et al. **STGRAT: A Spatio-Temporal Graph Attention Network for Traffic Forecasting**. AAAI 2020. [paper](https://arxiv.org/abs/1911.13181).

123. Liu Y, Wang X, Wu S, et al. **Independence Promoted Graph Disentangled Networks**. AAAI 2020. [paper](https://arxiv.org/abs/1911.11430).

123. Shi H, Fan H, Kwok J T. **Effective Decoding in Graph Auto-Encoder using Triadic Closure**. AAAI 2020. [paper](https://arxiv.org/pdf/1911.11322.pdf).

124. Wang X, Wang R, Shi C, et al. **Multi-Component Graph Convolutional Collaborative Filtering**. AAAI 2020. [paper](https://arxiv.org/pdf/1911.10699.pdf).

125. Su J, Beling P A, Guo R, et al. **Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration**. AAAI 2020. [paper](https://arxiv.org/pdf/1911.09837.pdf).

126. Claudio Gallicchio and Alessio Micheli. **Fast and Deep Graph Neural Networks**. AAAI 2020. [paper](https://arxiv.org/pdf/1911.08941.pdf).

127. Peng W, Hong X, Chen H, et al. **Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching**. AAAI 2020. [paper](https://arxiv.org/pdf/1911.04131.pdf).

128. Paliwal A, Loos S, Rabe M, et al. **Graph Representations for Higher-Order Logic and Theorem Proving**. AAAI 2020. [paper](https://arxiv.org/pdf/1905.10006.pdf).

129. Kenta Oono, et al. **Graph Neural Networks Exponentially Lose Expressive Power for Node Classification**. ICLR 2020. [paper](https://openreview.net/pdf?id=S1ldO2EFPr).

130. Muhan Zhang, et al. **Inductive Matrix Completion Based on Graph Neural Networks**. ICLR 2020. [paper](https://openreview.net/pdf?id=ByxxgCEYDS).

131. Pablo Barceló, et al. **The Logical Expressiveness of Graph Neural Networks**. ICLR 2020. [paper](https://openreview.net/pdf?id=r1lZ7AEKvB)

132. Weihua Hu, et al. **Strategies for Pre-training Graph Neural Networks**. ICLR 2020. [paper](https://openreview.net/pdf?id=HJlWWJSFDH)

133. Hongbin Pei, et al. **Geom-GCN: Geometric Graph Convolutional Networks**. ICLR 2020. [paper](https://openreview.net/pdf?id=S1e2agrFvS)

134. Ze Ye, et al. **Curvature Graph Network**. ICLR 2020. [paper](https://openreview.net/pdf?id=BylEqnVFDB)

135. Andreas Loukas, et al. **What graph neural networks cannot learn: depth vs width**. ICLR 2020. [paper](https://openreview.net/forum?id=B1l2bp4YwS)

136. Federico Errica, et al. **A Fair Comparison of Graph Neural Networks for Graph Classification**. ICLR 2020. [paper](https://openreview.net/pdf?id=HygDF6NFPB)

137. Kai Zhang, et al. **Adaptive Structural Fingerprints for Graph Attention Networks**. ICLR 2020. [paper](https://openreview.net/pdf?id=BJxWx0NYPr)

138. Shikhar Vashishth, et al. **Composition-based Multi-Relational Graph Convolutional Networks**. ICLR 2020. [paper](https://openreview.net/pdf?id=BylA_C4tPr)

139. Jiayi Wei, et al. **LambdaNet: Probabilistic Type Inference using Graph Neural Networks**. ICLR 2020. [paper](https://openreview.net/pdf?id=Hkx6hANtwH)

140. Jiechuan Jiang, et al. **Graph Convolutional Reinforcement Learning**. ICLR 2020. [paper](https://openreview.net/pdf?id=HkxdQkSYDB)

141. Yifan Hou, et al. **Measuring and Improving the Use of Graph Information in Graph Neural Networks**. ICLR 2020. [paper](https://openreview.net/pdf?id=rkeIIkHKvS)

142. Ruochi Zhang, et al. **Hyper-SAGNN: a self-attention based graph neural network for hypergraphs**. ICLR 2020. [paper](https://openreview.net/pdf?id=ryeHuJBtPH)

143. Yu Rong, et al. **DropEdge: Towards Deep Graph Convolutional Networks on Node Classification**. ICLR 2020. [paper](https://openreview.net/pdf?id=Hkx1qkrKPr)

144. Yuyu Zhang, et al. **Efficient Probabilistic Logic Reasoning with Graph Neural Networks**. ICLR 2020. [paper](https://openreview.net/pdf?id=rJg76kStwH)

145. Amir hosein Khasahmadi, et al. **Memory-based graph networks**. ICLR 2020. [paper](https://openreview.net/pdf?id=r1laNeBYPB)

146. Zeng, Hanqing, et al. **GraphSAINT: Graph Sampling Based Inductive Learning Method.** ICLR 2020. [paper](https://arxiv.org/pdf/1907.04931.pdf)

147. Jiangke Lin, et al. **Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks**. CVPR 2020. [paper](https://arxiv.org/pdf/2003.05653.pdf)

148. Oytun Ulutan, et al. **VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions**. CVPR 2020. [paper](https://arxiv.org/pdf/2003.05541.pdf)

149. Qiangeng Xu, et al. **Grid-GCN for Fast and Scalable Point Cloud Learning**. CVPR 2020. [paper](https://arxiv.org/pdf/1912.02984.pdf)

150. Abduallah Mohamed and Kun Qian, **Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction**. CVPR 2020. [paper](https://arxiv.org/pdf/2002.11927.pdf)

151. Kaihua Zhang, et al. **Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection**. CVPR 2020. [paper](https://arxiv.org/pdf/2003.06167.pdf)

152. Jiaming Shen, et al. **TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network**. WWW 2020. [paper](https://arxiv.org/pdf/2001.09522.pdf)

153. Deyu Bo, et al. **Structural Deep Clustering Network**. WWW 2020. [paper](https://arxiv.org/pdf/2002.01633.pdf)

154. Xinyu Fu, et al. **MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding**. WWW 2020. [paper](https://arxiv.org/pdf/2002.01680.pdf)

155. Man Wu, et al. **Unsupervised Domain Adaptive Graph Convolutional Networks**. WWW 2020. [paper](https://www.researchgate.net/publication/338844424_Unsupervised_Domain_Adaptive_Graph_Convolutional_Networks)

156. Yiwei Sun, et al. **Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach**. WWW 2020. [paper](https://dl.acm.org/doi/pdf/10.1145/3366423.3380149)

157. Xiaoyang Wang, et al. **Traffic Flow Prediction via Spatial Temporal Graph Neural Network**. WWW 2020. [paper](https://doi.org/10.1145/3366423.3380186)

158. Qiaoyu Tan, et al. **Learning to Hash with Graph Neural Networks for Recommender Systems**. WWW 2020. [paper](https://doi.org/10.1145/3366423.3380266)

159. Liang Qu, et al. **Continuous-Time Link Prediction via Temporal Dependent Graph Neural Network**. WWW 2020. [paper](https://dl.acm.org/doi/pdf/10.1145/3366423.3380073)

160. Wei Jin, et al. **Graph Structure Learning for Robust Graph Neural Networks**. KDD 2020. [paper](https://arxiv.org/pdf/2005.10203.pdf), [code](https://github.com/ChandlerBang/Pro-GNN).

161. Zonghan Wu, et al. **Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks**. KDD 2020. [paper](https://shiruipan.github.io/publication/kdd-2020-wu/kdd-2020-wu.pdf).

162. Zhen Yang, et al. **Understanding Negative Sampling in Graph Representation Learning**. KDD 2020. [paper](https://arxiv.org/pdf/2005.09863.pdf).

163. Menghan Wang, et al. **M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems**. KDD 2020. [paper](https://arxiv.org/pdf/2005.10110.pdf).

164. Louis-Pascal A. C. Xhonneux, et al. **Continuous Graph Neural Networks**. ICML 2020. [paper](https://arxiv.org/pdf/1912.00967.pdf).

165. Marc Brockschmidt, et al. **GNN-FiLM: Graph Neural Networks with Feature-wise Linear Modulation**. ICML 2020. [paper](https://proceedings.mlr.press/v119/brockschmidt20a.html).

166. Arman Hasanzadeh, et al. **Bayesian Graph Neural Networks with Adaptive Connection Sampling**. ICML 2020. [paper](https://proceedings.mlr.press/v119/hasanzadeh20a.html).

167. Filipe de Avila Belbute-Peres, et al. **Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction**. ICML 2020. [paper](https://proceedings.mlr.press/v119/de-avila-belbute-peres20a.html).

168. Ilay Luz, et al. **Learning Algebraic Multigrid Using Graph Neural Networks**. ICML 2020. [paper](http://proceedings.mlr.press/v119/luz20a.html).

169. Vikas K Garg, et al. **Generalization and Representational Limits of Graph Neural Networks**. ICML 2020. [paper](http://proceedings.mlr.press/v119/garg20c.html).

170. Shuai Zhang, et al. **Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case**. ICML 2020. [paper](http://proceedings.mlr.press/v119/zhang20y.html).

171. Filippo, et al. **Spectral Clustering with Graph Neural Networks for Graph Pooling**. ICML 2020. [paper](https://proceedings.mlr.press/v119/bianchi20a.html).

172. Ming Chen, et al. **Simple and Deep Graph Convolutional Networks**. ICML 2020. [paper](https://proceedings.mlr.press/v119/chen20v.html).

173. Yuning You, et al. **When Does Self-Supervision Help Graph Convolutional Networks?**. ICML 2020. [paper](http://proceedings.mlr.press/v119/you20a.html).

174. Gregor Bachmann, et al. **Constant Curvature Graph Convolutional Networks**. ICML 2020. [paper](http://proceedings.mlr.press/v119/bachmann20a.html).

175. Wenhui Yu, et al. **Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters**. ICML 2020. [paper](http://proceedings.mlr.press/v119/yu20e.html).

176. Hongmin Zhu, et al. **Bilinear Graph Neural Network with Neighbor Interactions**. IJCAI 2020. [paper](https://arxiv.org/abs/2002.03575).

177. Shuo Zhang, et al. **Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation**. IJCAI 2020. [paper](https://arxiv.org/abs/1907.02204).

178. Kaixiong Zhou, et al. **Multi-Channel Graph Neural Networks**. IJCAI 2020. [paper](https://www.ijcai.org/Proceedings/2020/188).

179. George Dasoulas, et al. **Coloring Graph Neural Networks for Node Disambiguation**. IJCAI 2020. [paper](https://doi.org/10.24963/ijcai.2020/294).

180. Xuan Lin, et al. **KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction**. IJCAI 2020. [paper](https://www.ijcai.org/Proceedings/2020/380).

181. Yuan Zhuang, et al. **Smart Contract Vulnerability Detection using Graph Neural Network**. IJCAI 2020. [paper](https://www.ijcai.org/Proceedings/2020/454).

182. Ziyu Jia, et al. **GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification**. IJCAI 2020. [paper](https://www.ijcai.org/Proceedings/2020/184).

183. Zhichao Huang, et al. **MR-GCN: Multi-Relational Graph Convolutional Networks based on Generalized Tensor Product**. IJCAI 2020. [paper](https://www.ijcai.org/Proceedings/2020/175).

184. Rongzhou Huang, et al. **LSGCN: Long Short-Term Traffic Prediction with Graph Convolutional Networks**. IJCAI 2020. [paper](https://www.ijcai.org/Proceedings/2020/326).

185. Min Shi, et al. **Multi-Class Imbalanced Graph Convolutional Network Learning**. IJCAI 2020. [paper](https://www.ijcai.org/Proceedings/2020/398).

186. Dongxiao He, et al. **Community-Centric Graph Convolutional Network for Unsupervised Community Detection**. IJCAI 2020. [paper](https://www.ijcai.org/Proceedings/2020/486).

187. Luana Ruiz et al. **Graphon Neural Networks and the Transferability of Graph Neural Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/12bcd658ef0a540cabc36cdf2b1046fd-Abstract.html)

188. Diego Mesquita et al. **Rethinking pooling in graph neural networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/1764183ef03fc7324eb58c3842bd9a57-Abstract.html)

189. Petar Veličković et al. **Pointer Graph Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/176bf6219855a6eb1f3a30903e34b6fb-Abstract.html)

190. Andreas Loukas. **How hard is to distinguish graphs with graph neural networks?**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/23685a2431acad7789c1e3d43ea1522c-Abstract.html)

191. Shangchen Zhou et al. **Cross-Scale Internal Graph Neural Network for Image Super-Resolution**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/23ad3e314e2a2b43b4c720507cec0723-Abstract.html)

192. Jiaqi Ma et al. **Towards More Practical Adversarial Attacks on Graph Neural Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/32bb90e8976aab5298d5da10fe66f21d-Abstract.html)

193. Kaixiong Zhou et al. **Towards Deeper Graph Neural Networks with Differentiable Group Normalization**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/33dd6dba1d56e826aac1cbf23cdcca87-Abstract.html)

194. Benjamin Sanchez-Lengeling et al. **Evaluating Attribution for Graph Neural Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/417fbbf2e9d5a28a855a11894b2e795a-Abstract.html)

195. Ziqi Liu et al. **Bandit Samplers for Training Graph Neural Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/4cea2358d3cc5f8cd32397ca9bc51b94-Abstract.html)

196. Jiong Zhu et al. **Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/58ae23d878a47004366189884c2f8440-Abstract.html)

197. Emily Alsentzer et al. **Subgraph Neural Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/5bca8566db79f3788be9efd96c9ed70d-Abstract.html)

198. Zhen Zhang et al. **Factor Graph Neural Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/61c66a2f4e6e10dc9c16ddf9d19745d6-Abstract.html)

199. Xiang Zhang et al. **GNNGuard: Defending Graph Neural Networks against Adversarial Attacks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/690d83983a63aa1818423fd6edd3bfdb-Abstract.html)

200. Zhengdao Chen et al. **Can Graph Neural Networks Count Substructures?**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/75877cb75154206c4e65e76b88a12712-Abstract.html)

201. Fangda Gu et al. **Implicit Graph Neural Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/8b5c8441a8ff8e151b191c53c1842a38-Abstract.html)

202. Minh Vu et al. **PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/8fb134f258b1f7865a6ab2d935a897c9-Abstract.html)

203. Simon Geisler et al. **Reliable Graph Neural Networks via Robust Aggregation**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/99e314b1b43706773153e7ef375fc68c-Abstract.html)

204. Clément Vignac et al. **Building powerful and equivariant graph neural networks with structural message-passing**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/a32d7eeaae19821fd9ce317f3ce952a7-Abstract.html)

205. Ming Chen et al. **Scalable Graph Neural Networks via Bidirectional Propagation**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/a7789ef88d599b8df86bbee632b2994d-Abstract.html)

206. Giannis Nikolentzos et al. **Random Walk Graph Neural Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/ba95d78a7c942571185308775a97a3a0-Abstract.html)

207. Zheng Ma et al. **Path Integral Based Convolution and Pooling for Graph Neural Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/be53d253d6bc3258a8160556dda3e9b2-Abstract.html)

208. Jiaxuan You et al. **Design Space for Graph Neural Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/c5c3d4fe6b2cc463c7d7ecba17cc9de7-Abstract.html)

209. Defu Cao et al. **Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/cdf6581cb7aca4b7e19ef136c6e601a5-Abstract.html)

210. Kenta Oono et al. **Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/dab49080d80c724aad5ebf158d63df41-Abstract.html)

211. Yu Chen et al. **Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/e05c7ba4e087beea9410929698dc41a6-Abstract.html)

212. Dongsheng Luo et al. **Parameterized Explainer for Graph Neural Network**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/e37b08dd3015330dcbb5d6663667b8b8-Abstract.html)

213. Martin Klissarov et al. **Reward Propagation Using Graph Convolutional Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/970627414218ccff3497cb7a784288f5-Abstract.html)

214. Yimeng Min et al. **Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/a6b964c0bb675116a15ef1325b01ff45-Abstract.html)

215. LEI BAI et al. **Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/ce1aad92b939420fc17005e5461e6f48-Abstract.html)

216. Moshe Eliasof et al. **DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/d16a974d4d6d0d71b29bfbfe045f1da7-Abstract.html)

217. Pantelis Elinas et al. **Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/d882050bb9eeba930974f596931be527-Abstract.html)

218. Yiding Yang et al. **Factorizable Graph Convolutional Networks**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/ea3502c3594588f0e9d5142f99c66627-Abstract.html)

219. Nicolas Keriven et al. **Convergence and Stability of Graph Convolutional Networks on Large Random Graphs**. NeurIPS 2020. [paper](https://proceedings.neurips.cc/paper/2020/hash/f5a14d4963acf488e3a24780a84ac96c-Abstract.html)

220. Chen K, Niu M, Chen Q. **A Hierarchical Reasoning Graph Neural Network for The Automatic Scoring of Answer Transcriptions in Video Job Interviews**. AAAI 2021. [paper](https://arxiv.org/abs/2012.11960)

221. Liang Yang, et al. **Why Do Attributes Propagate in Graph Convolutional Neural Networks?**. AAAI 2021, [paper](https://ojs.aaai.org/index.php/AAAI/article/view/16588)

222. Xueyang Fu, et al. **Rain Streak Removal via Dual Graph Convolutional Network**. AAAI 2021, [paper](https://ojs.aaai.org/index.php/AAAI/article/view/16224)

223. Inhwan Bae et al. **Disentangled Multi-Relational Graph Convolutional Network for Pedestrian Trajectory Prediction**. AAAI 2021, [paper](https://ojs.aaai.org/index.php/AAAI/article/view/16174)

224. Xin Xia et al. **Self-Supervised Hypergraph Convolutional Networks for Session-Based Recommendation**. AAAI 2021, [paper](https://arxiv.org/abs/2012.06852)

225. Sheng Wan et al. **Contrastive and Generative Graph Convolutional Networks for Graph-Based SemiSupervised Learning**. AAAI 2021, [paper](https://arxiv.org/abs/2009.07111)

226. Zhan Chen et al. **Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition**. AAAI 2021, [paper](https://ojs.aaai.org/index.php/AAAI/article/view/16197)

227. Xin Chen et al. **Fitting the Search Space of Weight-Sharing NAS with Graph Convolutional Networks**. AAAI 2021, [paper](https://arxiv.org/abs/2004.08423)

228. Qingbao Huang et al. **Story Ending Generation with Multi-Level Graph Convolutional Networks over Dependency Trees**. AAAI 2021, [paper](https://ojs.aaai.org/index.php/AAAI/article/view/17545)

229. Heng Chang et al. **Power up! Robust Graph Convolutional Network via Graph Powering**. AAAI 2021, [paper](https://people.eecs.berkeley.edu/~sojoudi/Robust_GCN.pdf)

230. Deyu Bo et al. **Beyond Low-Frequency Information in Graph Convolutional Networks**. AAAI 2021, [paper](https://arxiv.org/abs/2101.00797)

231. Han Yang et al. **Rethinking Graph Regularization for Graph Neural Networks**. AAAI 2021, [paper](https://arxiv.org/abs/2009.02027)

232. Tong Zhao et al. **Data Augmentation for Graph Neural Networks**. AAAI 2021, [paper](https://arxiv.org/abs/2006.06830)

233. Jiaxuan You et al. **Identity-Aware Graph Neural Networks**. AAAI 2021, [paper](https://arxiv.org/abs/2101.10320)

234. Yuanfu Lu et al. **Learning to Pre-Train Graph Neural Networks**. AAAI 2021, [paper](http://shichuan.org/doc/101.pdf)

235. Q Li et al. **Learning Graph Neural Networks with Approximate Gradient Descent**. AAAI 2021, [paper](https://arxiv.org/abs/2012.03429)

236. Yuankai Wu et al. **Inductive Graph Neural Networks for Spatiotemporal Kriging**. AAAI 2021, [paper](https://arxiv.org/abs/2006.07527)

237. Jiong Zhu et al. **Graph Neural Networks with Heterophily**. AAAI 2021, [paper](https://arxiv.org/abs/2009.13566)

238. Mengzhang Li et al. **Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting**. AAAI 2021, [paper](https://arxiv.org/abs/2012.09641)

239. Shengsheng Qian et al. **Dual Adversarial Graph Neural Networks for Multi-Label Cross-Modal Retrieval**. AAAI 2021, [paper](https://ojs.aaai.org/index.php/AAAI/article/view/16345)

240. Mengzhang Li et al. **Graph Neural Network-Based Anomaly Detection in Multivariate Time Series**. AAAI 2021, [paper](https://ojs.aaai.org/index.php/AAAI/article/view/16523)

241. Fan Zhou et al. **Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay**. AAAI 2021, [paper](https://arxiv.org/abs/2012.06002)

242. Georgios Panagopoulos et al. **Transfer Graph Neural Networks for Pandemic Forecasting**. AAAI 2021, [paper](https://www.researchgate.net/publication/344294392_Transfer_Graph_Neural_Networks_for_Pandemic_Forecasting)

243. Uday Shankar Shanthamallu et al. **Uncertainty-Matching Graph Neural Networks to Defend against Poisoning Attacks**. AAAI 2021, [paper](https://arxiv.org/abs/2009.14455)

244. Jianan Zhao et al. **Heterogeneous Graph Structure Learning for Graph Neural Networks**. AAAI 2021, [paper](http://shichuan.org/doc/100.pdf)

245. Yanan Zhang et al. **PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection**. AAAI 2021, [paper](https://arxiv.org/abs/2012.10412)

246. Tengfei Song et al. **Uncertain Graph Neural Networks for Facial Action Unit Detection**. AAAI 2021, [paper](https://www.researchgate.net/publication/346853340_Uncertain_Graph_Neural_Networks_for_Facial_Action_Unit_Detection)

247. Li Sun et al. **Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs**. AAAI 2021, [paper](https://ojs.aaai.org/index.php/AAAI/article/view/16563))

248. Binghui Wang et al. **Semi-Supervised Node Classification on Graphs: Markov Random Fields vs. Graph Neural Networks**. AAAI 2021, [paper](https://arxiv.org/abs/2012.13085)

249. Arijit Sehanobish et al. **Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-Supervised Edge Features and Graph Neural Networks**. AAAI 2021, [paper](https://arxiv.org/abs/2006.12971)

250. Utkarsh Desai et al. **Graph Neural Network to Dilute Outliers for Refactoring Monolith Application**. AAAI 2021, [paper](https://ojs.aaai.org/index.php/AAAI/article/view/16079))

251. Huihui Liu et al. **Overcoming Catastrophic Forgetting in Graph Neural Networks**. AAAI 2021, [paper](https://arxiv.org/abs/2012.06002)

252. Yuhang Yao et al. **Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks**. AAAI 2021, [paper](https://arxiv.org/abs/2012.08740)

253. Daizong Liu et al. **Spatiotemporal Graph Neural Network Based Mask Reconstruction for Video Object Segmentation**. AAAI 2021, [paper](https://arxiv.org/abs/2012.05499)

254. Cai T et al. **Graphnorm: A principled approach to accelerating graph neural network training**. ICML 2021. [paper](https://arxiv.org/pdf/2009.03294.pdf)

255. Baranwal A et al. **Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization**. ICML 2021. [paper](https://arxiv.org/pdf/2102.06966.pdf)

256. Hang M et al. **A Collective Learning Framework to Boost GNN Expressiveness**. ICML 2021. [paper](https://arxiv.org/pdf/2003.12169.pdf)

257. Henderson R et al. **Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity**. ICML 2021. [paper](https://arxiv.org/pdf/2105.04854.pdf)

258. Fey M et al. **GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings**. ICML 2021. [paper](https://arxiv.org/pdf/2106.05609.pdf)

259. Guo-Sen Xie et al. **Scale-Aware Graph Neural Network for Few-Shot Semantic Segmentation**. CVPR 2021. [paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Xie_Scale-Aware_Graph_Neural_Network_for_Few-Shot_Semantic_Segmentation_CVPR_2021_paper.pdf)

260. Kai Fischer et al. **StickyPillars: Robust and Efficient Feature Matching on Point Clouds Using Graph Neural Networks**. CVPR 2021. [paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Fischer_StickyPillars_Robust_and_Efficient_Feature_Matching_on_Point_Clouds_Using_CVPR_2021_paper.pdf)

261. Yiding Yang et al. **Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking**. CVPR 2021. [paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Yang_Learning_Dynamics_via_Graph_Neural_Networks_for_Human_Pose_Estimation_CVPR_2021_paper.pdf)

262. Guillaume Jaume et al. **Quantifying Explainers of Graph Neural Networks in Computational Pathology**. CVPR 2021. [paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Jaume_Quantifying_Explainers_of_Graph_Neural_Networks_in_Computational_Pathology_CVPR_2021_paper.pdf)

263. Shaofei Cai, et al. **Rethinking Graph Neural Architecture Search From Message-Passing**. CVPR 2021. [paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Cai_Rethinking_Graph_Neural_Architecture_Search_From_Message-Passing_CVPR_2021_paper.pdf)

264. Yongcheng Jing, et al. **Amalgamating Knowledge From Heterogeneous Graph Neural Networks**. CVPR 2021. [paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Jing_Amalgamating_Knowledge_From_Heterogeneous_Graph_Neural_Networks_CVPR_2021_paper.pdf)

265. Mehdi Bahri, et al. **Binary Graph Neural Networks**. CVPR 2021. [paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Bahri_Binary_Graph_Neural_Networks_CVPR_2021_paper.pdf)

266. Liushuai Shi, et al. **SGCN: Sparse Graph Convolution Network for Pedestrian Trajectory Prediction**. CVPR 2021. [paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Shi_SGCN_Sparse_Graph_Convolution_Network_for_Pedestrian_Trajectory_Prediction_CVPR_2021_paper.pdf)

267. Dongyu She, et al. **Hierarchical Layout-Aware Graph Convolutional Network for Unified Aesthetics Assessment**. CVPR 2021. [paper](https://openaccess.thecvf.com/content/CVPR2021/papers/She_Hierarchical_Layout-Aware_Graph_Convolutional_Network_for_Unified_Aesthetics_Assessment_CVPR_2021_paper.pdf)

268. Jindou Dai, et al. **A Hyperbolic-to-Hyperbolic Graph Convolutional Network**. CVPR 2021. [paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Dai_A_Hyperbolic-to-Hyperbolic_Graph_Convolutional_Network_CVPR_2021_paper.pdf)

269. Junfu Wang, et al. **Bi-GCN: Binary Graph Convolutional Network**. CVPR 2021. [paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_Bi-GCN_Binary_Graph_Convolutional_Network_CVPR_2021_paper.pdf)

270. Razvan Caramalau, et al. **Sequential Graph Convolutional Network for Active Learning**. CVPR 2021. [paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Caramalau_Sequential_Graph_Convolutional_Network_for_Active_Learning_CVPR_2021_paper.pdf)

271. Keyulu Xu, et al. **How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks**. ICLR 2021. [paper](https://openreview.net/pdf?id=UH-cmocLJC)

272. Waiss Azizian, et al. **Expressive Power of Invariant and Equivariant Graph Neural Networks**. ICLR 2021. [paper](https://openreview.net/pdf?id=lxHgXYN4bwl)

### ![#1589F0](https://via.placeholder.com/15/1589F0/000000?text=+) `Progress in 2022 Conference Papers`:
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
**Novel GNN methods proposed in 2022**

274. **Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples**. AAAI 2022. [paper](https://www.aaai.org/AAAI22Papers/AAAI-4442.DuanW.pdf)

274. **Block Modeling-Guided Graph Convolutional Neural Networks**. AAAI 2022. [paper](https://arxiv.org/pdf/2112.13507.pdf)

275. **Deformable Graph Convolutional Networks**. AAAI 2022. [paper](https://arxiv.org/pdf/2112.14438.pdf)

276. **ProtGNN: Towards Self-Explaining Graph Neural Networks**. AAAI 2022. [paper](https://arxiv.org/pdf/2112.00911.pdf)

277. **Adaptive Kernel Graph Neural Network**. AAAI 2022. [paper](https://arxiv.org/pdf/2112.04575.pdf)

278. **Self-supervised Graph Neural Networks via Diverse and Interactive Message Passing**. AAAI 2022. [paper](https://yangliang.github.io/pdf/aaai22.pdf)

279. **A Self-Supervised Mixed-Curvature Graph Neural Network**. AAAI 2022. [paper](https://arxiv.org/pdf/2112.05393.pdf)

280. **KerGNNs: Interpretable Graph Neural Networks with Graph Kernels**. AAAI 2022. [paper](https://arxiv.org/pdf/2201.00491.pdf)

281. **Orthogonal Graph Neural Networks**. AAAI 2022. [paper](https://arxiv.org/pdf/2109.11338.pdf)

282. **SAIL: Self-Augmented Graph Contrastive Learning**. AAAI 2022. [paper](https://arxiv.org/pdf/2009.00934.pdf)

283. **AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators**. AAAI 2022. [paper](https://arxiv.org/pdf/2109.10259.pdf)

284. **Adversarial Graph Contrastive Learning with Information Regularization**. WWW 2022. [paper](https://arxiv.org/pdf/2202.06491.pdf)

285. **Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift**. WWW 2022. [paper](https://arxiv.org/pdf/2201.11349.pdf)

286. **Curvature Graph Generative Adversarial Networks**. WWW 2022. [paper](https://arxiv.org/pdf/2203.01604.pdf)

287. **Dual Space Graph Contrastive Learning**. WWW 2022. [paper](https://arxiv.org/pdf/2201.07409.pdf)

288. **GBK-GNN: Gated Bi-Kernel Graph Neural Network for Modeling Both Homophily and Heterophily**. WWW 2022. [paper](https://arxiv.org/pdf/2110.15777.pdf)

289. **Geometric Graph Representation Learning via Maximizing Rate Reduction**. WWW 2022. [paper](https://arxiv.org/pdf/2202.06241.pdf)

290. **Graph Communal Contrastive Learning**. WWW 2022. [paper](https://arxiv.org/pdf/2110.14863.pdf)

291. **Graph Neural Networks Beyond Compromise Between Attribute and Topology**. WWW 2022. [paper](https://yangliang.github.io/pdf/www22.pdf)

292. **Graph-adaptive Rectified Linear Unit for Graph Neural Networks**. WWW 2022. [paper](https://arxiv.org/pdf/2202.06281.pdf)

293. **Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily**. WWW 2022. [paper](https://arxiv.org/pdf/2202.06281.pdf)

294. **Polarized Graph Neural Networks**. WWW 2022. [Temporarily unavailable]

295. **On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks**. WWW 2022. [paper](https://zemin-liu.github.io/papers/SOLT-GNN-WWW-22.pdf)

296. **SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation**. WWW 2022. [paper](https://arxiv.org/pdf/2202.03104.pdf)

297. **Towards Unsupervised Deep Graph Structure Learning**. WWW 2022. [paper](https://arxiv.org/pdf/2201.06367.pdf)

298. **Expressiveness and Approximation Properties of Graph Neural Networks**. ICLR 2022. [paper](https://openreview.net/pdf?id=wIzUeM3TAU)

299. **A New Perspective on "How Graph Neural Networks Go Beyond Weisfeiler-Lehman?"**. ICLR 2022. [paper](https://openreview.net/pdf?id=uxgg9o7bI_3)

299. **p-Laplacian Based Graph Neural Networks**. ICML 2022. [paper](https://proceedings.mlr.press/v162/fu22e/fu22e.pdf)

299. **Going Deeper into Permutation-Sensitive Graph Neural Networks**. ICML 2022. [paper](https://proceedings.mlr.press/v162/huang22l/huang22l.pdf)

299. **SE(3) Equivariant Graph Neural Networks with Complete Local Frames**. ICML 2022. [paper](https://proceedings.mlr.press/v162/du22e/du22e.pdf)

299. **A New Perspective on the Effects of Spectrum in Graph Neural Networks**. ICML 2022. [paper](https://proceedings.mlr.press/v162/yang22n/yang22n.pdf)

299. **How Powerful are Spectral Graph Neural Networks**. ICML 2022. [paper](https://proceedings.mlr.press/v162/wang22am/wang22am.pdf)

299. **Local Augmentation for Graph Neural Networks**. ICML 2022. [paper](https://proceedings.mlr.press/v162/liu22s/liu22s.pdf)

299. **Graph Neural Network Training and Data Tiering**. KDD 2022. [paper](https://dl-acm-org.ndfpz.top/doi/pdf/10.1145/3534678.3539038)

299. **Model Degradation Hinders Deep Graph Neural Networks**. KDD 2022. [paper](https://dl-acm-org.geray.top/doi/pdf/10.1145/3534678.3539374)

299. **Improving Social Network Embedding via New Second-Order Continuous Graph Neural Networks**. KDD 2022. [paper](https://dl-acm-org.geray.top/doi/pdf/10.1145/3534678.3539415)

299. **Graph Neural Networks with Node-wise Architecture**. KDD 2022. [paper](https://dl-acm-org.geray.top/doi/pdf/10.1145/3534678.3539387)

299. **GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks**. KDD 2022. [paper](https://dl-acm-org.geray.top/doi/pdf/10.1145/3534678.3539249)

299. **How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications**. KDD 2022. [paper](https://dl-acm-org.geray.top/doi/pdf/10.1145/3534678.3539418)

299. **Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage**. KDD 2022. [paper](https://dl-acm-org.ndfpz.top/doi/pdf/10.1145/3534678.3539404)

299. **Hierarchical Diffusion Scattering Graph Neural Network**. IJCAI 2022. [paper](https://www.ijcai.org/proceedings/2022/0519.pdf)

299. **RAW-GNN: RAndom Walk Aggregation based Graph Neural Network**. IJCAI 2022. [paper](https://www.ijcai.org/proceedings/2022/0293.pdf)

299. **Survey on Graph Neural Network Acceleration: An Algorithmic Perspective**. IJCAI 2022. [paper](https://www.ijcai.org/proceedings/2022/0772.pdf)

299. **Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification**. IJCAI 2022. [paper](https://www.ijcai.org/proceedings/2022/0272.pdf)

318. **Hybrid Graph Neural Networks for Few-Shot Learning**. AAAI 2022. [paper](https://arxiv.org/pdf/2112.06538.pdf)

299. **Qubit Routing Using Graph Neural Network Aided Monte Carlo Tree Search**. AAAI 2022. [paper](https://arxiv.org/pdf/2104.01992.pdf)

299. **CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting**. AAAI 2022. [paper](https://www.aaai.org/AAAI22Papers/AISI-6475.WangL.pdf)

299. **LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks**. AAAI 2022. [paper](https://www.aaai.org/AAAI22Papers/AAAI-51.GoodgeA.pdf)

299. **DDGCN: Dual Dynamic Graph Convolutional Networks for Rumor Detection on Social Media**. AAAI 2022. [paper](https://www.aaai.org/AAAI22Papers/AAAI-6370.SunM.pdf)

299. **Low-Pass Graph Convolutional Network for Recommendation**. AAAI 2022. [paper](https://www.aaai.org/AAAI22Papers/AAAI-3643.WenhuiY.pdf)

299. **Learning to Detect 3D Facial Landmarks via Heatmap Regression with Graph Convolutional Network**. AAAI 2022. [paper](https://www.aaai.org/AAAI22Papers/AAAI-4116.WangY.pdf)

299. **GeomGCL: Geometric Graph Contrastive Learning for Molecular Property Prediction**. AAAI 2022. [paper](https://arxiv.org/pdf/2109.11730.pdf)

299. **AUC-oriented Graph Neural Network for Fraud Detection**. WWW 2022. [paper](https://ponderly.github.io/pub/AOGNN_WWW2022.pdf)

299. **Molecular Representation Learning via Heterogeneous Motif Graph Neural Networks**. ICML 2022. [paper](https://proceedings.mlr.press/v162/yu22a/yu22a.pdf)

299. **Rethinking Graph Neural Networks for Anomaly Detection**. ICML 2022. [paper](https://proceedings.mlr.press/v162/tang22b/tang22b.pdf)

299. **DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting**. ICML 2022. [paper](https://proceedings.mlr.press/v162/lan22a/lan22a.pdf)

299. **Motif Prediction with Graph Neural Networks**. KDD 2022. [paper](https://dl-acm-org.nhobg.top/doi/pdf/10.1145/3534678.3539343)

299. **Graph Neural Networks in Life Sciences: Opportunities and Solutions**. KDD 2022. [paper](https://dl-acm-org.nhobg.top/doi/pdf/10.1145/3534678.3542628)

299. **Graph2Route: A Dynamic Spatial-Temporal Graph Neural Network for Pick-up and Delivery Route Prediction**. KDD 2022. [paper](https://dl-acm-org.ndfpz.top/doi/pdf/10.1145/3534678.3539084)

299. **Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks**. IJCAI 2022. [paper](https://www.ijcai.org/proceedings/2022/0482.pdf)

299. **Self-supervised Graph Neural Networks for Multi-behavior Recommendation**. IJCAI 2022. [paper](https://www.ijcai.org/proceedings/2022/0285.pdf)

299. **RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation**. IEEE INFORCOM 2022. [paper](https://ieeexplore.ieee.org/document/9796944)

299. **PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images**. CVPR 2022. [paper](https://ieeexplore.ieee.org/document/9880425)

## ![#f03c15](https://via.placeholder.com/15/f03c15/000000?text=+) `ArXiv papers`:

1. Li Y, Tarlow D, Brockschmidt M, et al. **Gated graph sequence neural networks**. arXiv 2015. [paper](https://arxiv.org/abs/1511.05493)

2. Henaff M, Bruna J, LeCun Y. **Deep convolutional networks on graph-structured data**, arXiv 2015. [paper](https://arxiv.org/abs/1506.05163)

3. Hechtlinger Y, Chakravarti P, Qin J. **A generalization of convolutional neural networks to graph-structured data**. arXiv 2017. [paper](https://arxiv.org/abs/1704.08165)

4. Marcheggiani D, Titov I. **Encoding sentences with graph convolutional networks for semantic role labeling**. arXiv 2017. [paper](https://arxiv.org/pdf/1703.04826.pdf)

5. Battaglia P W, Hamrick J B, Bapst V, et al. **Relational inductive biases, deep learning, and graph networks**, arXiv 2018. [paper](http://xueshu.baidu.com/s?wd=paperuri%3A%28965a67685f0a5180ada2c6adaa15dc38%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Farxiv.org%2Fpdf%2F1806.01261&ie=utf-8&sc_us=8121954972779087681&sc_as_para=sc_lib%3A)

6. Verma S, Zhang Z L. **Graph Capsule Convolutional Neural Networks**. arXiv 2018. [paper](https://arxiv.org/pdf/1805.08090.pdf)

7. Zhang T , Zheng W , Cui Z , et al. **Tensor graph convolutional neural network**. arXiv 2018. [paper](https://arxiv.org/pdf/1803.10071.pdf)

8. Zou D, Lerman G. **Graph Convolutional Neural Networks via Scattering**. arXiv 2018. [paper](http://xueshu.baidu.com/s?wd=paperuri%3A%287cdb7eb72b8fb59d6b831fe7b9dd3cc6%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Farxiv.org%2Fpdf%2F1804.00099&ie=utf-8&sc_us=16682575734158616085&sc_as_para=sc_lib%3A)

9. Du J , Zhang S , Wu G , et al. **Topology Adaptive Graph Convolutional Networks**. arXiv 2018. [paper](https://arxiv.org/pdf/1710.10370.pdf).

10. Shang C , Liu Q , Chen K S , et al. **Edge Attention-based Multi-Relational Graph Convolutional Networks**. arXiv 2018. [paper](https://arxiv.org/abs/1802.04944).

11. Scardapane S , Vaerenbergh S V , Comminiello D , et al. **Improving Graph Convolutional Networks with Non-Parametric Activation Functions**. arXiv 2018. [paper](https://arxiv.org/pdf/1802.09405.pdf).

12. Wang Y , Sun Y , Liu Z , et al. **Dynamic Graph CNN for Learning on Point Clouds**. arXiv 2018. [paper](https://arxiv.org/pdf/1801.07829.pdf).

13. Ryu S , Lim J , Hong S H , et al. **Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network**. arXiv 2018. [paper](https://arxiv.org/abs/1805.10988).

14. Cui Z , Henrickson K , Ke R , et al. **High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting**. arXiv 2018. [paper](https://arxiv.org/abs/1802.07007).

15. Shchur O , Mumme M , Bojchevski A , et al. **Pitfalls of Graph Neural Network Evaluation**. arXiv 2018. [paper](https://arxiv.org/abs/1811.05868).

16. Bai Y , Ding H , Bian S , et al. **Graph Edit Distance Computation via Graph Neural Networks**. arXiv 2018. [paper](https://arxiv.org/abs/1808.05689).

17. Pedro H. C. Avelar, Henrique Lemos, Marcelo O. R. Prates, Luis Lamb, **Multitask Learning on Graph Neural Networks - Learning Multiple Graph Centrality Measures with a Unified Network**. arXiv 2018. [paper](https://arxiv.org/pdf/1809.07695.pdf).

18. Matthew Baron, **Topology and Prediction Focused Research on Graph Convolutional Neural Networks**. arXiv 2018. [paper](https://arxiv.org/pdf/1808.07769.pdf).

19. Wenting Zhao, Chunyan Xu, Zhen Cui, Tong Zhang, Jiatao Jiang, Zhenyu Zhang, Jian Yang, **When Work Matters: Transforming Classical Network Structures to Graph CNN**. arXiv 2018. [paper](https://arxiv.org/pdf/1807.02653.pdf).

20. Xavier Bresson, Thomas Laurent, **Residual Gated Graph ConvNets**. arXiv 2018. [paper](https://arxiv.org/pdf/1711.07553.pdf).

21. Kun XuLingfei WuZhiguo WangYansong FengVadim Sheinin, **Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks**. arXiv 2018. [paper](https://arxiv.org/pdf/1804.00823.pdf).

22. Xiaojie GuoLingfei WuLiang Zhao. **Deep Graph Translation**. arXiv 2018. [paper](https://arxiv.org/pdf/1805.09980.pdf).

21. Choma, Nicholas, et al. **Graph Neural Networks for IceCube Signal Classification.** ArXiv 2018. [paper](https://arxiv.org/pdf/1809.06166.pdf).

22. Tyler Derr, Yao Ma, Jiliang Tang. **Signed Graph Convolutional Network** ArXiv 2018. [paper](https://arxiv.org/pdf/1809.06166.pdf).

23. Yawei Luo, Tao Guan, Junqing Yu, Ping Liu, Yi Yang. **Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning** ArXiv 2018. [paper](https://arxiv.org/pdf/1809.09925.pdf).

24. Sun K, Koniusz P, Wang J. **Fisher-Bures Adversary Graph Convolutional Networks**. arXiv 2019. [paper](https://arxiv.org/pdf/1903.04154.pdf).

25. Kazi A, Burwinkel H, Vivar G, et al. **InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction**. arXiv 2019. [paper](https://arxiv.org/abs/1903.04233).

26. Lemos H, Prates M, Avelar P, et al. **Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems**. arXiv 2019. [paper](https://arxiv.org/pdf/1903.04598.pdf).

27. Diehl F, Brunner T, Le M T, et al. **Graph Neural Networks for Modelling Traffic Participant Interaction**. arXiv 2019. [paper](https://arxiv.org/abs/1903.01254).

28. Murphy R L, Srinivasan B, Rao V, et al. **Relational Pooling for Graph Representations**. arXiv 2019. [paper](https://arxiv.org/pdf/1903.02541.pdf).

29. Zhang W, Shu K, Liu H, et al. **Graph Neural Networks for User Identity Linkage**. arXiv 2019. [paper](https://arxiv.org/pdf/1903.02174.pdf).

30. Ruiz L, Gama F, Ribeiro A. **Gated Graph Convolutional Recurrent Neural Networks**. arXiv 2019. [paper](https://arxiv.org/pdf/1903.01888.pdf).

31. Phillips S, Daniilidis K. **All Graphs Lead to Rome: Learning Geometric and Cycle-Consistent Representations with Graph Convolutional Networks**. arXiv 2019. [paper](https://arxiv.org/abs/1901.02078).

32. Hu F, Zhu Y, Wu S, et al. **Semi-supervised Node Classification via Hierarchical Graph Convolutional Networks**. arXiv 2019. [paper](https://arxiv.org/abs/1902.06667).

33. Deng Z, Dong Y, Zhu J. **Batch Virtual Adversarial Training for Graph Convolutional Networks**. arXiv 2019. [paper](https://arxiv.org/abs/1902.09192).

34. Chen Z M, Wei X S, Wang P, et al.**Multi-Label Image Recognition with Graph Convolutional Networks**. arXiv 2019. [paper](https://arxiv.org/abs/1904.03582).

35. Mallea M D G, Meltzer P, Bentley P J. **Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations**. arXiv 2019. [paper](https://arxiv.org/pdf/1902.08399.pdf).

36. Peter Meltzer, Marcelo Daniel Gutierrez Mallea and Peter J. Bentley. **PiNet: A Permutation Invariant Graph Neural Network for Graph Classification**. arXiv 2019. [paper](https://arxiv.org/pdf/1905.03046.pdf).

37. Padraig Corcoran. **Function Space Pooling For Graph Convolutional Networks**. arXiv 2019. [paper](https://arxiv.org/pdf/1905.06259.pdf).

38. Sbastien Lerique, Jacob Levy Abitbol, and Mrton Karsai. **Joint embedding of structure and features via graph convolutional networks**. arXiv 2019. [paper](https://arxiv.org/pdf/1905.08636.pdf).

39. Chen D, Lin Y, Li W, et al. **Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View**. arXiv 2019. [paper](https://arxiv.gg363.site/abs/1909.03211)

40. Ohue M, Ii R, Yanagisawa K, et al. **Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph**. arXiv 2019. [paper](https://arxiv.org/pdf/1907.01103.pdf).

41. Gao X, Xiong H, Frossard P. **iPool--Information-based Pooling in Hierarchical Graph Neural Networks**. arXiv 2019. [paper](https://arxiv.org/pdf/1907.00832.pdf).

42. Zhou K, Song Q, Huang X, et al. **Auto-GNN: Neural Architecture Search of Graph Neural Networks**. arXiv 2019. [paper](https://arxiv.org/pdf/1909.03184.pdf).

43. Vijay Prakash Dwivedi, et al. **Benchmarking Graph Neural Networks**. arXiv 2020. [paper](https://arxiv.org/pdf/2003.00982.pdf).

44. Dai Quoc Nguyen, Tu Dinh Nguyen, Dinh Phung. **Universal Self-Attention Network for Graph Classification**. arXiv 2020. [paper](https://arxiv.org/pdf/1909.11855.pdf)

## ![#f03c15](https://via.placeholder.com/15/f03c15/000000?text=+) `Open source platforms on GNN`:

1. **Deep Graph Library（DGL)**

DGL is developed and maintained by New York University, New York University Shanghai, AWS Shanghai Research Institute and AWS MXNet Science Team.

Initiation time: 2018.

Source: [URL](https://www.dgl.ai/), [github](https://github.com/jermainewang/dgl)

2. **NGra**

NGra is developed and maintained by Peking University and Microsoft Asia Research Institute.

Initiation time:2018

Source: [pdf](https://arxiv.org/pdf/1810.08403.pdf)

3. **Graph_nets**

Graph_nets is developed and maintained by [DeepMind](https://deepmind.com/), Google Corp.

Initiation time:2018

Source: [github](https://github.com/deepmind/graph_nets)

4. **Euler**

Euler is developed and maintained by Alimama, which belongs to Alibaba Group.

Initiation time:2019

Source: [github](https://github.com/alibaba/euler)

5. **PyTorch Geometric**

PyTorch Geometric is developed and maintained by TU Dortmund University, Germany.

Initiation time:2019

Source: [github](https://github.com/rusty1s/pytorch_geometric) [paper](https://arxiv.org/abs/1903.02428?context=cs.LG)

6. **PyTorch-BigGraph（PBG）**

PBG is developed and maintained by Facebook AI Research.

Initiation time:2019

Source: [github](https://github.com/facebookresearch/PyTorch-BigGraph) [paper](https://arxiv.org/abs/1903.12287)

7. **Angel**

Angel is developed and maintained by Tencent Inc.

Initiation time:2019

Source: [github](https://github.com/Angel-ML/angel)

8. **Plato**

Plato is developed and maintained by Tencent Inc.

Initiation time:2019

Source: [github](https://github.com/tencent/plato)

9. **PGL**

PGL is developed and maintained by Baidu Inc.

Initiation time:2019

Source: [github](https://github.com/PaddlePaddle/PGL)

10. **OGB**

Open Graph Benchmark(OGB) is developed and maintained by Standford University.

Initiation time:2019

Source: [github](http://ogb.stanford.edu)

11. **Benchmarking GNNs**

Benchmarking GNNs is developed and maintained by Nanyang Technological University.

Initiation time:2020

Source: [github](https://github.com/graphdeeplearning/benchmarking-gnns)

12. **Graph-Learn**

Graph-Learn is developed and maintained by Alibaba Group.

Initiation time:2020

Source: [github](https://github.com/alibaba/graph-learn)

13. **AutoGL (Auto Graph Learning)** **New**

AutoGL is developed and maintained by Tsinghua University.

Initiation time:2020

Source: [github](https://github.com/THUMNLab/AutoGL)

## ![#f03c15](https://via.placeholder.com/15/f03c15/000000?text=+) `Appetizer：Art Exhibition of Network/Graph Structured Data`:

![image](https://github.com/jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress/blob/master/Fig3.gif)

1. **The interesting Social Network**.

![image](https://github.com/jdlc105/Must-read-papers-and-continuous-tracking-on-Graph-Neural-Network-GNN-progress/blob/master/Fig4.jpg)

2. **The beauty of the Biological Network**.