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https://github.com/eric-erki/awesome-graph-classification
A collection of important graph embedding, classification and representation learning papers with implementations.
https://github.com/eric-erki/awesome-graph-classification
List: awesome-graph-classification
Last synced: 16 days ago
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A collection of important graph embedding, classification and representation learning papers with implementations.
- Host: GitHub
- URL: https://github.com/eric-erki/awesome-graph-classification
- Owner: eric-erki
- License: cc0-1.0
- Created: 2020-02-02T10:04:18.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-02-02T10:04:42.000Z (almost 5 years ago)
- Last Synced: 2024-05-21T00:19:51.926Z (7 months ago)
- Language: Python
- Size: 1.64 MB
- Stars: 2
- Watchers: 3
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: contributing.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: code-of-conduct.md
Awesome Lists containing this project
- ultimate-awesome - awesome-graph-classification - A collection of important graph embedding, classification and representation learning papers with implementations. (Other Lists / PowerShell Lists)
README
# Awesome Graph Classification
[](https://github.com/sindresorhus/awesome)
[](http://makeapullrequest.com)
A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers with reference implementations.
Relevant graph classification benchmark datasets are available [[here]](https://github.com/shiruipan/graph_datasets).
Similar collections about [community detection](https://github.com/benedekrozemberczki/awesome-community-detection), [classification/regression tree](https://github.com/benedekrozemberczki/awesome-decision-tree-papers), [fraud detection](https://github.com/benedekrozemberczki/awesome-fraud-detection-papers), [Monte Carlo tree search](https://github.com/benedekrozemberczki/awesome-monte-carlo-tree-search-papers), and [gradient boosting](https://github.com/benedekrozemberczki/awesome-gradient-boosting-papers) papers with implementations.
##### Contents
1. [Factorization](#factorization)
2. [Spectral and Statistical Fingerprints](#spectral-and-statistical-fingerprints)
3. [Deep Learning](#deep-learning)
4. [Graph Kernels](#graph-kernels)
## Factorization
- **GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features (ICONIP 2019)**
- Hong Chen, Hisashi Koga
- [[Paper]](https://link.springer.com/chapter/10.1007/978-3-030-36718-3_1)
- [[Python Reference]](https://github.com/benedekrozemberczki/karateclub/)
- **Hierarchical Stochastic Graphlet Embedding for Graph-based Pattern Recognition (Pattern Recognition 2018)**
- Anjan Dutta, Pau Riba, Josep Lladós, Alicia Fornés
- [[Paper]](https://arxiv.org/abs/1807.02839)
- [[Matlab Reference]](https://github.com/priba/hierarchicalSGE)
- **Learning Graph Representation via Frequent Subgraphs (SDM 2018)**
- Dang Nguyen, Wei Luo, Tu Dinh Nguyen, Svetha Venkatesh, Dinh Phung
- [[Paper]](https://epubs.siam.org/doi/10.1137/1.9781611975321.35)
- [[Python Reference]](https://github.com/nphdang/GE-FSG)
- **Anonymous Walk Embeddings (ICML 2018)**
- Sergey Ivanov and Evgeny Burnaev
- [[Paper]](https://arxiv.org/pdf/1805.11921.pdf)
- [[Python Reference]](https://github.com/nd7141/AWE)
- **Graph2vec (MLGWorkshop 2017)**
- Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan
- [[Paper]](https://arxiv.org/abs/1707.05005)
- [[Python High Performance]](https://github.com/benedekrozemberczki/graph2vec)
- [[Python Reference]](https://github.com/MLDroid/graph2vec_tf)
- **Subgraph2Vec (MLGWorkshop 2016)**
- Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan
- [[Paper]](https://arxiv.org/abs/1606.08928)
- [[Python High Performance]](https://github.com/MLDroid/subgraph2vec_gensim)
- [[Python Reference]](https://github.com/MLDroid/subgraph2vec_tf)
- **Rdf2Vec: RDF Graph Embeddings for Data Mining (ISWC 2016)**
- Petar Ristoski and Heiko Paulheim
- [[Paper]](https://link.springer.com/chapter/10.1007/978-3-319-46523-4_30)
- [[Python Reference]](https://github.com/airobert/RDF2VecAtWebScale)
- **Deep Graph Kernels (KDD 2015)**
- Pinar Yanardag and S.V.N. Vishwanathan
- [[Paper]](https://dl.acm.org/citation.cfm?id=2783417)
- [[Python Reference]](https://github.com/pankajk/Deep-Graph-Kernels)
## Spectral and Statistical Fingerprints
- **A Simple Yet Effective Baseline for Non-Attribute Graph Classification (ICLR RLPM 2019)**
- Chen Cai, Yusu Wang
- [[Paper]](https://arxiv.org/abs/1811.03508)
- [[Python Reference]](https://github.com/Chen-Cai-OSU/LDP)
- **NetLSD (KDD 2018)**
- Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, and Emmanuel Müller
- [[Paper]](https://arxiv.org/abs/1805.10712)
- [[Python Reference]](https://github.com/xgfs/NetLSD)
- **A Simple Baseline Algorithm for Graph Classification (Relational Representation Learning, NIPS 2018)**
- Nathan de Lara and Edouard Pineau
- [[Paper]](https://arxiv.org/pdf/1810.09155.pdf)
- [[Python]](https://karateclub.readthedocs.io/)
- [[Python]](https://github.com/edouardpineau/A-simple-baseline-algorithm-for-graph-classification)
- **Multi-Graph Multi-Label Learning Based on Entropy (Entropy NIPS 2018)**
- Zixuan Zhu and Yuhai Zhao
- [[Paper]](https://github.com/TonyZZX/MultiGraph_MultiLabel_Learning/blob/master/entropy-20-00245.pdf)
- [[Python Reference]](https://github.com/TonyZZX/MultiGraph_MultiLabel_Learning)
- **Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs (NIPS 2017)**
- Saurabh Verma and Zhi-Li Zhang
- [[Paper]](https://papers.nips.cc/paper/6614-hunt-for-the-unique-stable-sparse-and-fast-feature-learning-on-graphs.pdf)
- [[Matlab Reference]](https://github.com/vermaMachineLearning/FGSD)
- [[Python Reference]](https://github.com/benedekrozemberczki/karateclub/)
- **Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification (TKDE 2015)**
- Shirui Pan, Jia Wu, Xingquan Zhuy, Chengqi Zhang, and Philip S. Yuz
- [[Paper]](https://ieeexplore.ieee.org/document/7302040)
- [[Java Reference]](https://github.com/shiruipan/MTG)
- **NetSimile: A Scalable Approach to Size-Independent Network Similarity (arXiv 2012)**
- Michele Berlingerio, Danai Koutra, Tina Eliassi-Rad, and Christos Faloutsos
- [[Paper]](https://arxiv.org/abs/1209.2684)
- [[Python]](https://github.com/kristyspatel/Netsimile)
## Deep Learning
- **Building Attention and Edge Convolution Neural Networks for Bioactivity and Physical-Chemical Property Prediction (AAAI 2020)**
- Michael Withnall, Edvard Lindelöf, Ola Engkvist, Hongming Chen
- [[Paper]](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-019-0407-y)
- [[Python Reference]](https://github.com/edvardlindelof/graph-neural-networks-for-drug-discovery)
- **GSSNN: Graph Smoothing Splines Neural Network (AAAI 2020)**
- Shichao Zhu, Lewei Zhou, Shirui Pan, Chuan Zhou, Guiying Yan, Bin Wang
- [[Paper]](https://shiruipan.github.io/publication/aaai-2020-zhu)
- [[Python Reference]](https://github.com/CheriseZhu/GSSNN)
- **Graph Convolutional Networks with EigenPooling (KDD 2019)**
- Yao Ma, Suhang Wang, Charu C Aggarwal, Jiliang Tang
- [[Paper]](https://arxiv.org/pdf/1904.13107.pdf)
- [[Python Reference]](https://github.com/alge24/eigenpooling)
- **Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels (NeurIPS 2019)**
- Simon S. Du, Kangcheng Hou, Barnabás Póczos, Ruslan Salakhutdinov, Ruosong Wang, Keyulu Xu
- [[Paper]](https://arxiv.org/abs/1905.13192)
- [[Python Reference]](https://github.com/KangchengHou/gntk)
- **Molecule Property Prediction Based on Spatial Graph Embedding (Journal of Cheminformatics Models 2019)**
- Xiaofeng Wang, Zhen Li, Mingjian Jiang, Shuang Wang, Shugang Zhang, Zhiqiang Wei
- [[Paper]](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b00410)
- [[Python Reference]](https://github.com/1128bian/C-SGEN)
- **Unsupervised Universal Self-Attention Network for Graph Classification (Arxiv 2019)**
- Dai Quoc Nguyen, Tu Dinh Nguyen, and Dinh Phun
- [[Paper]](https://arxiv.org/abs/1909.11855)
- [[Python Reference]](https://github.com/daiquocnguyen/U2GNN)
- **Fast Training of Sparse Graph Neural Networks on Dense Hardware (Arxiv 2019)**
- Matej Balog, Bart van Merriënboer, Subhodeep Moitra, Yujia Li, Daniel Tarlow
- [[Paper]](https://arxiv.org/abs/1906.11786)
- [[Python Reference]](https://github.com/anonymous-authors-iclr2020/fast_training_of_sparse_graph_neural_networks_on_dense_hardware)
- **Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling (Arxiv 2019)**
- Filippo Maria Bianchi, Daniele Grattarola, Lorenzo Livi, Cesare Alippi
- [[Paper]](https://arxiv.org/abs/1910.11436)
- [[Python Reference]](https://github.com/danielegrattarola/decimation-pooling)
- **Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification (Arxiv 2019)**
- Ting Chen, Song Bian, Yizhou Sun
- [[Paper]](https://arxiv.org/abs/1905.04579)
- [[Python Reference]](https://github.com/Waterpine/vis_network)
- **Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification (ECML-PKDD 2019)**
- Lu Bai, Yuhang Jiao, Lixin Cui, Edwin R. Hancock
- [[Paper]](https://arxiv.org/abs/1904.04238)
- [[Python Reference]](https://github.com/baiuoy/ASGCN_ECML-PKDD2019)
- **Relational Pooling for Graph Representations (ICML 2019)**
- Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao, Bruno Ribeiro
- [[Paper]](https://arxiv.org/abs/1903.02541)
- [[Python Reference]](https://github.com/PurdueMINDS/RelationalPooling)
- **Ego-CNN: Distributed, Egocentric Representations of Graphs for Detecting Critical Structure (ICML 2019)**
- Ruo-Chun Tzeng, Shan-Hung Wu
- [[Paper]](http://proceedings.mlr.press/v97/tzeng19a/tzeng19a.pdf)
- [[Python Reference]](https://github.com/rutzeng/EgoCNN)
- **Self-Attention Graph Pooling (ICML 2019)**
- Junhyun Lee, Inyeop Lee, Jaewoo Kang
- [[Paper]](https://arxiv.org/abs/1904.08082)
- [[Python Reference]](https://github.com/inyeoplee77/SAGPool)
- **Variational Recurrent Neural Networks for Graph Classification (ICLR 2019)**
- Edouard Pineau, Nathan de Lara
- [[Paper]](https://arxiv.org/abs/1902.02721)
- [[Python Reference]](https://github.com/edouardpineau/Variational-Recurrent-Neural-Networks-for-Graph-Classification)
- **Crystal Graph Neural Networks for Data Mining in Materials Science (Arxiv 2019)**
- Takenori Yamamoto
- [[Paper]](https://storage.googleapis.com/rimcs_cgnn/cgnn_matsci_May_27_2019.pdf)
- [[Python Reference]](https://github.com/Tony-Y/cgnn)
- **Explainability Techniques for Graph Convolutional Networks (ICML 2019 Workshop)**
- Federico Baldassarre, Hossein Azizpour
- [[Paper]](https://128.84.21.199/pdf/1905.13686.pdf)
- [[Python Reference]](https://github.com/gn-exp/gn-exp)
- **Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019)**
- Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, and Junzhou Huang
- [[Paper]](https://arxiv.org/pdf/1904.05003.pdf)
- [[Python Reference]](https://github.com/benedekrozemberczki/SEAL-CI)
- **Capsule Graph Neural Network (ICLR 2019)**
- Zhang Xinyi and Lihui Chen
- [[Paper]](https://openreview.net/forum?id=Byl8BnRcYm)
- [[Python Reference]](https://github.com/benedekrozemberczki/CapsGNN)
- **How Powerful are Graph Neural Networks? (ICLR 2019)**
- Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka
- [[Paper]](https://arxiv.org/abs/1810.00826)
- [[Python Reference]](https://github.com/weihua916/powerful-gnns)
- **Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (AAAI 2019)**
- Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe
- [[Paper]](https://arxiv.org/pdf/1810.02244v2.pdf)
- [[Python Reference]](https://github.com/k-gnn/k-gnn)
- **Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations (Arxiv 2019)**
- Marcelo Daniel Gutierrez Mallea, Peter Meltzer, and Peter J Bentley
- [[Paper]](https://arxiv.org/pdf/1902.08399v1.pdf)
- [[Python Reference]](https://github.com/BraintreeLtd/PatchyCapsules)
- **Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction (NIPS 2019)**
- Roei Herzig, Moshiko Raboh, Gal Chechik, Jonathan Berant, Amir Globerson
- [[Paper]](https://arxiv.org/abs/1802.05451)
- [[Python Reference]](https://github.com/shikorab/SceneGraph)
- **Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks (The Journal of Physical Chemistry Letters 2018)**
- Masashi Tsubaki and Teruyasu Mizoguchi
- [[Paper]](https://pubs.acs.org/doi/10.1021/acs.jpclett.8b01837)
- [[Python Reference]](https://github.com/masashitsubaki/molecularGNN_3Dstructure)
- **Machine Learning for Organic Cage Property Prediction (Chemical Matters 2018)**
- Lukas Turcani, Rebecca Greenway, Kim Jelfs
- [[Paper]](https://pubs.acs.org/doi/10.1021/acs.chemmater.8b03572)
- [[Python Reference]](https://github.com/qyuan7/Graph_Convolutional_Network_for_cages)
- **Three-Dimensionally Embedded Graph Convolutional Network for Molecule Interpretation (Arxiv 2018)**
- Hyeoncheol Cho and Insung. S. Choi
- [[Paper]](https://arxiv.org/abs/1811.09794)
- [[Python Reference]](https://github.com/blackmints/3DGCN)
- **Learning Graph-Level Representations with Recurrent Neural Networks (Arxiv 2018)**
- Yu Jin and Joseph F. JaJa
- [[Paper]](https://arxiv.org/pdf/1805.07683v4.pdf)
- [[Python Reference]](https://github.com/yuj-umd/graphRNN)
- **Graph Capsule Convolutional Neural Networks (ICML 2018)**
- Saurabh Verma and Zhi-Li Zhang
- [[Paper]](https://arxiv.org/abs/1805.08090)
- [[Python Reference]](https://github.com/vermaMachineLearning/Graph-Capsule-CNN-Networks)
- **Graph Classification Using Structural Attention (KDD 2018)**
- John Boaz Lee, Ryan Rossi, and Xiangnan Kong
- [[Paper]](http://ryanrossi.com/pubs/KDD18-graph-attention-model.pdf)
- [[Python Pytorch Reference]](https://github.com/benedekrozemberczki/GAM)
- **Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation (NIPS 2018)**
- Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec
- [[Paper]](https://arxiv.org/abs/1806.02473)
- [[Python Reference]](https://github.com/bowenliu16/rl_graph_generation)
- **Hierarchical Graph Representation Learning with Differentiable Pooling (NIPS 2018)**
- Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton and Jure Leskovec
- [[Paper]](http://papers.nips.cc/paper/7729-hierarchical-graph-representation-learning-with-differentiable-pooling.pdf)
- [[Python Reference]](https://github.com/rusty1s/pytorch_geometric)
- **Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing (ICML 2018)**
- Davide Bacciu, Federico Errica, and Alessio Micheli
- [[Paper]](https://arxiv.org/pdf/1805.10636.pdf)
- [[Python Reference]](https://github.com/diningphil/CGMM)
- **MolGAN: An Implicit Generative Model for Small Molecular Graphs (ICML 2018)**
- Nicola De Cao and Thomas Kipf
- [[Paper]](https://arxiv.org/pdf/1805.11973.pdf)
- [[Python Reference]](https://github.com/nicola-decao/MolGAN)
- **Deeply Learning Molecular Structure-Property Relationships Using Graph Attention Neural Network (2018)**
- Seongok Ryu, Jaechang Lim, and Woo Youn Kim
- [[Paper]](https://arxiv.org/abs/1805.10988)
- [[Python Reference]](https://github.com/SeongokRyu/Molecular-GAT)
- **Compound-Protein Interaction Prediction with End-to-end Learning of Neural Networks for Graphs and Sequences (Bioinformatics 2018)**
- Masashi Tsubaki, Kentaro Tomii, and Jun Sese
- [[Paper]](https://academic.oup.com/bioinformatics/article/35/2/309/5050020)
- [[Python Reference]](https://github.com/masashitsubaki/CPI_prediction)
- [[Python Reference]](https://github.com/masashitsubaki/GNN_molecules)
- [[Python Alternative ]](https://github.com/xnuohz/GCNDTI)
- **Learning Graph Distances with Message Passing Neural Networks (ICPR 2018)**
- Pau Riba, Andreas Fischer, Josep Llados, and Alicia Fornes
- [[Paper]](https://ieeexplore.ieee.org/abstract/document/8545310)
- [[Python Reference]](https://github.com/priba/siamese_ged)
- **Edge Attention-based Multi-Relational Graph Convolutional Networks (2018)**
- Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi and Jinbo Bi
- [[Paper]](https://arxiv.org/abs/1802.04944v1)
- [[Python Reference]](https://github.com/Luckick/EAGCN)
- **Commonsense Knowledge Aware Conversation Generation with Graph Attention (IJCAI-ECAI 2018)**
- Hao Zhou, Tom Yang, Minlie Huang, Haizhou Zhao, Jingfang Xu and Xiaoyan Zhu
- [[Paper]](http://coai.cs.tsinghua.edu.cn/hml/media/files/2018_commonsense_ZhouHao_3_TYVQ7Iq.pdf)
- [[Python Reference]](https://github.com/tuxchow/ccm)
- **Residual Gated Graph ConvNets (ICLR 2018)**
- Xavier Bresson and Thomas Laurent
- [[Paper]](https://arxiv.org/pdf/1711.07553v2.pdf)
- [[Python Pytorch Reference]](https://github.com/xbresson/spatial_graph_convnets)
- **An End-to-End Deep Learning Architecture for Graph Classification (AAAI 2018)**
- Muhan Zhang, Zhicheng Cui, Marion Neumann and Yixin Chen
- [[Paper]](https://www.cse.wustl.edu/~muhan/papers/AAAI_2018_DGCNN.pdf)
- [[Python Tensorflow Reference]](https://github.com/muhanzhang/DGCNN)
- [[Python Pytorch Reference]](https://github.com/muhanzhang/pytorch_DGCNN)
- [[MATLAB Reference]](https://github.com/muhanzhang/DGCNN)
- [[Python Alternative]](https://github.com/leftthomas/DGCNN)
- [[Python Alternative]](https://github.com/hitlic/DGCNN-tensorflow)
- **SGR: Self-Supervised Spectral Graph Representation Learning (KDD DLDay 2018)**
- Anton Tsitsulin, Davide Mottin, Panagiotis Karra, Alex Bronstein and Emmanueal Müller
- [[Paper]](https://arxiv.org/abs/1807.02839)
- [[Python Reference]](http://mott.in/publications/others/sgr/)
- **Deep Learning with Topological Signatures (NIPS 2017)**
- Christoph Hofer, Roland Kwitt, Marc Niethammer, and Andreas Uhl
- [[paper]](https://arxiv.org/abs/1707.04041)
- [[Python Reference]](https://github.com/c-hofer/nips2017)
- **Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (CVPR 2017)**
- Martin Simonovsky and Nikos Komodakis
- [[paper]](https://arxiv.org/pdf/1704.02901v3.pdf)
- [[Python Reference]](https://github.com/mys007/ecc)
- **Deriving Neural Architectures from Sequence and Graph Kernels (ICML 2017)**
- Tao Lei, Wengong Jin, Regina Barzilay, and Tommi Jaakkola
- [[Paper]](https://arxiv.org/abs/1705.09037)
- [[Python Reference]](https://github.com/taolei87/icml17_knn)
- **Protein Interface Prediction using Graph Convolutional Networks (NIPS 2017)**
- Alex Fout, Jonathon Byrd, Basir Shariat and Asa Ben-Hur
- [[Paper]](https://papers.nips.cc/paper/7231-protein-interface-prediction-using-graph-convolutional-networks)
- [[Python Reference]](https://github.com/fouticus/pipgcn)
- **Graph Classification with 2D Convolutional Neural Networks (2017)**
- Antoine J.-P. Tixier, Giannis Nikolentzos, Polykarpos Meladianos and Michalis Vazirgiannis
- [[Paper]](https://arxiv.org/abs/1708.02218)
- [[Python Reference]](https://github.com/Tixierae/graph_2D_CNN)
- **CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters (IEEE TSP 2017)**
- Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein
- [[Paper]](https://arxiv.org/pdf/1705.07664v2.pdf)
- [[Python Reference]](https://github.com/fmonti/CayleyNet)
- **Semi-Supervised Learning of Hierarchical Representations of Molecules Using Neural Message Passing (2017)**
- Hai Nguyen, Shin-ichi Maeda, Kenta Oono
- [[Paper]](https://arxiv.org/pdf/1711.10168.pdf)
- [[Python Reference]](https://github.com/pfnet-research/hierarchical-molecular-learning)
- **Kernel Graph Convolutional Neural Networks (2017)**
- Giannis Nikolentzos, Polykarpos Meladianos, Antoine Jean-Pierre Tixier, Konstantinos Skianis, Michalis Vazirgiannis
- [[Paper]](https://arxiv.org/pdf/1710.10689.pdf)
- [[Python Reference]](https://github.com/giannisnik/cnn-graph-classification)
- **Deep Topology Classification: A New Approach For Massive Graph Classification (IEEE Big Data 2016)**
- Stephen Bonner, John Brennan, Georgios Theodoropoulos, Ibad Kureshi, Andrew Stephen McGough
- [[Paper]](https://ieeexplore.ieee.org/document/7840988/)
- [[Python Reference]](https://github.com/sbonner0/DeepTopologyClassification)
- **Learning Convolutional Neural Networks for Graphs (ICML 2016)**
- Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov
- [[Paper]](https://arxiv.org/abs/1605.05273)
- [[Python Reference]](https://github.com/tvayer/PSCN)
- **Gated Graph Sequence Neural Networks (ICLR 2016)**
- Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel
- [[Paper]](https://arxiv.org/abs/1511.05493)
- [[Python TensorFlow]](https://github.com/bdqnghi/ggnn.tensorflow)
- [[Python PyTorch]](https://github.com/JamesChuanggg/ggnn.pytorch)
- [[Python Reference]](https://github.com/YunjaeChoi/ggnnmols)
- **Convolutional Networks on Graphs for Learning Molecular Fingerprints (NIPS 2015)**
- David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P. Adams
- [[Paper]](https://papers.nips.cc/paper/5954-convolutional-networks-on-graphs-for-learning-molecular-fingerprints.pdf)
- [[Python Reference]](https://github.com/fllinares/neural_fingerprints_tf)
- [[Python Reference]](https://github.com/jacklin18/neural-fingerprint-in-GNN)
- [[Python Reference]](https://github.com/HIPS/neural-fingerprint)
- [[Python Reference]](https://github.com/debbiemarkslab/neural-fingerprint-theano)
## Graph Kernels
- **A Persistent Weisfeiler–Lehman Procedure for Graph Classification (ICML 2019)**
- Sebastian Rieck, Christian Bock, and Karsten Borgwardt
- [[Paper]](http://proceedings.mlr.press/v97/rieck19a/rieck19a.pdf)
- [[Python Reference]](https://github.com/BorgwardtLab/P-WL)
- **Message Passing Graph Kernels (2018)**
- Giannis Nikolentzos, Michalis Vazirgiannis
- [[Paper]](https://arxiv.org/pdf/1808.02510.pdf)
- [[Python Reference]](https://github.com/giannisnik/message_passing_graph_kernels)
- **Matching Node Embeddings for Graph Similarity (AAAI 2017)**
- Giannis Nikolentzos, Polykarpos Meladianos, and Michalis Vazirgiannis
- [[Paper]](https://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14494)
- **Global Weisfeiler-Lehman Graph Kernels (2017)**
- Christopher Morris, Kristian Kersting and Petra Mutzel
- [[Paper]](https://arxiv.org/pdf/1703.02379.pdf)
- [[C++ Reference]](https://github.com/chrsmrrs/glocalwl)
- **On Valid Optimal Assignment Kernels and Applications to Graph Classification (2016)**
- Nils Kriege, Pierre-Louis Giscard, Richard Wilson
- [[Paper]](https://arxiv.org/pdf/1606.01141.pdf)
- [[Java Reference]](https://github.com/nlskrg/optimal_assignment_kernels)
- **Efficient Comparison of Massive Graphs Through The Use Of ‘Graph Fingerprints’ (MLGWorkshop 2016)**
- Stephen Bonner, John Brennan, and A. Stephen McGough
- [[Paper]](http://dro.dur.ac.uk/19773/1/19773.pdf?DDD10+lzdh59+d700tmt)
- [[python Reference]](https://github.com/sbonner0/GraphFingerprintComparison)
- **The Multiscale Laplacian Graph Kernel (NIPS 2016)**
- Risi Kondor and Horace Pan
- [[Paper]](https://arxiv.org/abs/1603.06186)
- [[C++ Reference]](https://github.com/horacepan/MLGkernel)
- **Faster Kernels for Graphs with Continuous Attributes (ICDM 2016)**
- Christopher Morris, Nils M. Kriege, Kristian Kersting and Petra Mutzel
- [[Paper]](https://arxiv.org/abs/1610.00064)
- [[Python Reference]](https://github.com/chrsmrrs/hashgraphkernel)
- **Propagation Kernels: Efficient Graph Kernels From Propagated Information (Machine Learning 2016)**
- Neumann, Marion and Garnett, Roman and Bauckhage, Christian and Kersting, Kristian
- [[Paper]](https://link.springer.com/article/10.1007/s10994-015-5517-9)
- [[Matlab Reference]](https://github.com/marionmari/propagation_kernels)
- **Halting Random Walk Kernels (NIPS 2015)**
- Mahito Sugiyama and Karsten M. Borgward
- [[Paper]](https://pdfs.semanticscholar.org/79ba/8bcfbf9496834fdc22a1f7c96d26d776cd6c.pdf)
- [[C++ Reference]](https://github.com/BorgwardtLab/graph-kernels)
- **A Graph Kernel Based on the Jensen-Shannon Representation Alignment (IJCAI 2015)**
- Lu Bai, Zhihong Zhang, Chaoyan Wang, Xiao Bai, Edwin R. Hancock
- [[Paper]](http://ijcai.org/Proceedings/15/Papers/468.pdf)
- [[Matlab reference]](https://github.com/baiuoy/Matlab-code-JS-alignment-kernel-IJCAI-2015)
- **An Aligned Subtree Kernel for Weighted Graphs (ICML 2015)**
- Lu Bai, Luca Rossi, Zhihong Zhang, Edwin R. Hancock
- [[Paper]](http://proceedings.mlr.press/v37/bai15.pdf)
- **Scalable Kernels for Graphs with Continuous Attributes (NIPS 2013)**
- Aasa Feragen, Niklas Kasenburg, Jens Petersen, Marleen de Bruijne and Karsten Borgwardt
- [[Paper]](https://papers.nips.cc/paper/5155-scalable-kernels-for-graphs-with-continuous-attributes.pdf)
- **Subgraph Matching Kernels for Attributed Graphs (ICML 2012)**
- Nils Kriege and Petra Mutzel
- [[Paper]](https://arxiv.org/abs/1206.6483)
- [[Python Reference]](https://github.com/mockingbird2/GraphKernelBenchmark)
- **Nested Subtree Hash Kernels for Large-Scale Graph Classification over Streams (ICDM 2012)**
- Bin Li, Xingquan Zhu, Lianhua Chi, Chengqi Zhang
- [[Paper]](https://ieeexplore.ieee.org/document/6413884/)
- [[Python Reference]](https://github.com/benedekrozemberczki/NestedSubtreeHash)
- **Weisfeiler-Lehman Graph Kernels (JMLR 2011)**
- Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt
- [[Paper]](http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf)
- [[Python Reference]](https://github.com/jajupmochi/py-graph)
- [[Python Reference]](https://github.com/deeplego/wl-graph-kernels)
- [[C++ Reference]](https://github.com/BorgwardtLab/graph-kernels)
- **Two New Graphs Kernels in Chemoinformatics (Pattern Recognition Letters 2012)**
- Benoit Gaüzère, LucBrun, and Didier Villemin
- [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S016786551200102X)
- [[Python Reference]](https://github.com/jajupmochi/py-graph)
- **Fast Neighborhood Subgraph Pairwise Distance Kernel (ICML 2010)**
- Fabrizio Costa and Kurt De Grave
- [[Paper]](https://icml.cc/Conferences/2010/papers/347.pdf)
- [[C++ Reference]](www.bioinf.uni-freiburg.de/~costa/EDeNcpp.tgz)
- [[Python Reference]](https://github.com/fabriziocosta/EDeN)
- **Graph Kernels (JMLR 2010)**
- S.V.N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt;
- [[Paper]](http://www.jmlr.org/papers/volume11/vishwanathan10a/vishwanathan10a.pdf)
- [[Python Reference]](https://github.com/jajupmochi/py-graph)
- **A Linear-time Graph Kernel (ICDM 2009)**
- Shohei Hido and Hisashi Kashima
- [[Paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5360243)
- [[Python Reference]](https://github.com/hgascon/adagio)
- **Weisfeiler-Lehman Subtree Kernels (NIPS 2009)**
- Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt
- [[Paper]](http://papers.nips.cc/paper/3813-fast-subtree-kernels-on-graphs.pdf)
- [[Python Reference]](https://github.com/jajupmochi/py-graph)
- [[Python Reference]](https://github.com/deeplego/wl-graph-kernels)
- [[C++ Reference]](https://github.com/BorgwardtLab/graph-kernels)
- **Kernel on Bag of Paths For Measuring Similarity of Shapes (InESANN 2007)**
- Frederic Suard, Alain Rakotomamonjy, and Abdelaziz Bensrhair
- [[Paper]](https://pdfs.semanticscholar.org/149a/858889e8c3a54ee55b21511a7f56f5e9650b.pdf)
- [[Python Reference]](https://github.com/jajupmochi/py-graph)
- **Fast Computation of Graph Kernels (NIPS 2006)**
- S. V. N. Vishwanathan, Karsten M. Borgwardt, and Nicol N. Schraudolph
- [[Paper]](http://www.dbs.ifi.lmu.de/Publikationen/Papers/VisBorSch06.pdf)
- [[Python Reference]](https://github.com/jajupmochi/py-graph)
- [[C++ Reference]](https://github.com/BorgwardtLab/graph-kernels)
- **Shortest-Path Kernels on Graphs (ICDM 2005)**
- Karsten M. Borgwardt and Hans-Peter Kriegel
- [[Paper]](https://www.ethz.ch/content/dam/ethz/special-interest/bsse/borgwardt-lab/documents/papers/BorKri05.pdf)
- [[C++ Reference]](https://github.com/KitwareMedical/ITKTubeTK)
- **Graph Kernels for Chemical Informatics (Neural Networks 2005)**
- Liva Ralaivola, Sanjay J Swamidass, Hiroto Saigo, and Pierre Baldi
- [[Paper]](https://www.sciencedirect.com/science/article/pii/S0893608005001693)
- [[Python Reference]](https://github.com/jajupmochi/py-graph)
- **Cyclic Pattern Kernels For Predictive Graph Mining (KDD 2004)**
- Tamás Horváth, Thomas Gärtner, and Stefan Wrobel
- [[Paper]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.332.6158&rep=rep1&type=pdf)
- [[Python Reference]](https://github.com/jajupmochi/py-graph)
- **Extensions of Marginalized Graph Kernels (ICML 2004)**
- Pierre Mahe, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and Jean-Philippe Vert
- [[Paper]](http://members.cbio.mines-paristech.fr/~jvert/publi/04icml/icmlMod.pdf)
- [[Python Reference]](https://github.com/jajupmochi/py-graph)
- **Extensions of Marginalized Graph Kernels (ICML 2004)**
- Pierre Mahe , Nobuhisa Ueda , Tatsuya Akutsu , Jean-Luc Perret , Jean-Philippe Vert
- [[Paper]](http://members.cbio.mines-paristech.fr/~jvert/publi/04icml/icmlMod.pdf)
- [[Python Reference]](https://github.com/jajupmochi/py-graph)
- **Marginalized Kernels Between Labeled Graphs (ICML 2003)**
- Hisashi Kashima, Koji Tsuda, and Akihiro Inokuchi
- [[Paper]](https://pdfs.semanticscholar.org/2dfd/92c808487049ab4c9b45db77e9055b9da5a2.pdf)
- [[Python Reference]](https://github.com/jajupmochi/py-graph)
- **On Graph Kernels: Hardness Results and Efficient Alternatives (Learning Theory and Kernel Machines 2003)**
- Thomas Gärtner, Peter Flach, and Stefan Wrobel
- [[Paper]](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.152.8681&rep=rep1&type=pdf)
- [[Python Reference]](https://github.com/jajupmochi/py-graph)