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https://github.com/woojeongjin/dynamic-KG

Dynamic (Temporal) Knowledge Graph Completion (Reasoning)
https://github.com/woojeongjin/dynamic-KG

dynamic knowledge-base knowledge-graph knowledge-graph-completion temporal-data

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Dynamic (Temporal) Knowledge Graph Completion (Reasoning)

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# Dynamic Knowledge Graph Completion

This page is to summarize important materials about *dynamic (temporal) knowledge graph completion* and *dynamic graph embedding*.



# Bookmarks

- [Temporal Knowledge Graph Completion](#Temporal-knowledge-graph-completion-/-reasoning)

- [Dynamic Graph Embedding](#Dynamic-graph-embedding)

- [Knowledge Graph Embedding](#Knowledge-graph-embedding)

- [Static Graph Embedding](#Static-graph-embedding)

- [Survey](#Survey)

- [Others](#Others)

- [Useful Libararies](#Useful-Libararies)



## Temporal Knowledge Graph Completion / Reasoning

- [Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs](https://arxiv.org/abs/1904.05530)

	- Woojeong Jin, Meng Qu, Xisen Jin, Xiang Ren. EMNLP 2020.

		- This work is on an *extrapolation* problem which is to make predictions at unobserved times, different from interpolation work.

		- Proposes a novel neural architecture for modeling complex entity interaction sequences, which consists of a *recurrent event encoder* and a *neighborhood aggregator*.

		- Explores various neighborhood aggregators: a multi-relational graph aggregator demonstrates its effectiveness among them.

		- [Code and Data](https://github.com/INK-USC/re-net)



- [Learning Sequence Encoders for Temporal Knowledge Graph Completion](https://arxiv.org/abs/1809.03202) (Interpolation)

	- Alberto Garcia-Duran, Sebastijan Dumancic, Mathias Niepert. EMNLP 2018.

- [Towards time-aware knowledge graph completion](http://aclweb.org/anthology/C16-1161) (Interpolation)

	- Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Baobao Chang, Sujian Li and Zhifang Sui. COLING 2016.

- [Deriving validity time in knowledge graph](https://dl.acm.org/citation.cfm?id=3191639) (Interpolation)

	- Julien Leblay and Melisachew Wudage Chekol. WWW Workshop 2018.

- [HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding](http://aclweb.org/anthology/D18-1225) (Interpolation)

	- Shib Sankar Dasgupta, Swayambhu Nath Ray, Partha Talukdar. EMNLP 2018.

	- [Code (TF based)](https://github.com/malllabiisc/HyTE)

- [Predicting the co-evolution of event and knowledge graphs](https://arxiv.org/abs/1512.06900)

	- Cristóbal Esteban, Volker Tresp, Yinchong Yang, Stephan Baier, Denis Krompaß. FUSION 2016.

- [Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition](https://arxiv.org/pdf/1911.07893.pdf)

- [Diachronic Embedding for Temporal Knowledge Graph Completion](https://grlearning.github.io/papers/41.pdf)

- [Hybrid-TE: Hybrid Translation-based Temporal Knowledge Graph Embedding](http://www-kb.is.s.u-tokyo.ac.jp/~li-xin/ictai19.pdf)

- [Tensor Decompositions for Temporal Knowledge Base Completion](https://openreview.net/forum?id=rke2P1BFwS)



## Dynamic Graph Embedding

- [DyREP: Learning Representations over Dynamic Graphs](https://openreview.net/forum?id=HyePrhR5KX) (Extrapolation)

	- Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha. ICLR 2019.

- [DynGEM: Deep Embedding Method for Dynamic Graphs](https://arxiv.org/abs/1805.11273)

	- Palash Goyal, Nitin Kamra, Xinran He, Yan Liu. IJCAI 2017.

- [Graph2Seq: Scalable Learning Dynamics for Graphs](https://openreview.net/forum?id=Ske7ToC5Km)

	- Shaileshh Bojja Venkatakrishnan, Mohammad Alizadeh, Pramod Viswanath

- [Dynamic Graph Representation Learning via Self-Attention Networks](https://openreview.net/forum?id=HylsgnCcFQ)

	- Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, Hao Yang

- [Continuous-Time Dynamic Network Embeddings](http://ryanrossi.com/pubs/nguyen-et-al-WWW18-BigNet.pdf)

	- Giang Hoang Nguyen, John Boaz Lee, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim. WWW 2018.

- [GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction](https://arxiv.org/pdf/1812.04206.pdf)

	- Jinyin Chen, Xuanheng Xu, Yangyang Wu, Haibin Zheng

- [Learning Dynamic Embeddings from Temporal Interaction Networks](https://www-cs.stanford.edu/~srijan/pubs/paper-interactions.pdf)

	- Srijan Kumar, Xikun Zhang, Jure Leskovec

- [Dynamic Graph Convolutional Networks](https://arxiv.org/pdf/1704.06199.pdf)

	- Franco Manessi, Alessandro Rozza, Mario Manzo

- [Streaming Graph Neural Networks](https://arxiv.org/pdf/1810.10627.pdf)

	- Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin



- [Dynamic Network Embedding: An Extended Approach for Skip-gram based Network Embedding](https://www.ijcai.org/proceedings/2018/0288.pdf)

	- Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang

- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://arxiv.org/abs/1902.10191)

	- Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Charles E. Leisersen, ArXiv.

- [Gated Residual Recurrent Graph Neural Networks for Traffic Prediction](https://oar.a-star.edu.sg/jspui/bitstream/123456789/3020/1/AAAI-ChenC.4591.pdf#page8)

	- Cen Chen, Kenli Li, Sin G. Teo, Xiaofeng Zou, Kang Wang, Jie Wang, Zeng Zeng, AAAI 2019.

- [Structured Sequence Modeling with Graph Convolutional Recurrent Networks](https://arxiv.org/abs/1612.07659)

	- Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson, ICONIP 2017.

- [Dynamic Network Embedding by Modeling Triadic Closure Process](http://yangy.org/works/dynamictriad/dynamic_triad.pdf)

	- Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang. AAAI 2018.

- [DynGAN: Generative Adversarial Networks for Dynamic Network Embedding](https://grlearning.github.io/papers/66.pdf)

	- Ayush Maheshwari, Ayush Goyal, Manjesh Kumar Hanawal, Ganesh Ramakrishnan. NeurIPS 2019 Workshop.



## Knowledge Graph Embedding

- [Modeling Relational Data with Graph Convolutional Networks](https://arxiv.org/abs/1703.06103)

	- Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling. ESWC 2018.

	- [Code (Keras based)](https://github.com/tkipf/relational-gcn), [Code (TF based)](https://github.com/MichSchli/RelationPrediction)

- [Neural Relational Inference for Interacting Systems](https://arxiv.org/abs/1802.04687)

	- Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel. ICML 2018.

	- [Code (Pytorch based)](https://github.com/ethanfetaya/NRI)

- [Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs](https://arxiv.org/abs/1812.00279)

	- Daniel Neil, Joss Briody, Alix Lacoste, Aaron Sim, Paidi Creed, Amir Saffari. ICONIP 2017.



## Static Graph Embedding

- [Inductive Representation Learning on Large Graphs](https://www-cs-faculty.stanford.edu/people/jure/pubs/graphsage-nips17.pdf)

	- William L. Hamilton, Rex Ying, Jure Leskovec

	- [Code (TF based)](https://github.com/williamleif/GraphSAGE), [Code (Pytorch based)](https://github.com/williamleif/graphsage-simple/)

- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://arxiv.org/pdf/1806.01973)

	- Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec

- [Stochastic Training of Graph Convolutional Networks with Variance Reduction](https://arxiv.org/pdf/1710.10568.pdf)

	- Jianfei Chen, Jun Zhu, Le Song

- [A Higher-Order Graph Convolutional Layer](http://sami.haija.org/papers/high-order-gc-layer.pdf)

	- Sami Abu-El-Haija, Nazanin Alipourfard, Hrayr Harutyunyan, Amol Kapoor, Bryan Perozzi

- [Higher-order Graph Convolutional Networks](http://ryanrossi.com/pubs/Higher-order-GCNs.pdf)

	- John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, and Anup Rao



## Other Survey Papers

- [Deep Learning on Graphs: A Survey](https://arxiv.org/abs/1812.04202)

	- Ziwei Zhang, Peng Cui, Wenwu Zhu

- [Graph Neural Networks: A Review of Methods and Applications](https://arxiv.org/abs/1812.08434)

	- Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun

- [A Comprehensive Survey on Graph Neural Networks](https://arxiv.org/abs/1901.00596)

	- Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu

- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://arxiv.org/abs/1709.07604)

	- Hongyun Cai, Vincent W. Zheng, Kevin Chen-Chuan Chang

- [How Powerful are Graph Neural Networks?](https://arxiv.org/abs/1810.00826)

	- Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka. ICLR 2019.

- [Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey](https://arxiv.org/abs/1905.11485)

	- Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart



## Others

- [Temporal Convolutional Networks: A Unified Approach to Action Segmentation](https://arxiv.org/abs/1608.08242)

	- Colin Lea, Rene Vidal, Austin Reiter, Gregory D. Hager

- [What to Do Next: Modeling User Behaviors by Time-LSTM](https://www.ijcai.org/proceedings/2017/0504.pdf)

	- Yu Zhu, Hao Li, Yikang Liao, Beidou Wang, Ziyu Guan, Haifeng Liu, Deng Cai. IJCAI 2017.

- [Patient Subtyping via Time-Aware LSTM Networks](http://biometrics.cse.msu.edu/Publications/MachineLearning/Baytasetal_PatientSubtypingViaTimeAwareLSTMNetworks.pdf)

	- Inci M. Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, Jiayu Zhou. KDD 2017.



## Useful Libararies

- [Deep graph library](https://www.dgl.ai)

- [Pytorch geometric](https://github.com/rusty1s/pytorch_geometric)