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
• Dynamic Graph Embedding
• Knowledge Graph Embedding
• Static Graph Embedding
• Survey
• Others
• Useful Libararies

Temporal Knowledge Graph Completion

• Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
  • Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song. ICML 2017.
  • Video
  • Code (cpp)
• Learning Sequence Encoders for Temporal Knowledge Graph Completion
  • Alberto Garcia-Duran, Sebastijan Dumancic, Mathias Niepert. EMNLP 2018.
• Towards time-aware knowledge graph completion
  • Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Baobao Chang, Sujian Li and Zhifang Sui. COLING 2016
• Predicting the co-evolution of event and knowledge graphs
  • Cristóbal Esteban, Volker Tresp, Yinchong Yang, Stephan Baier, Denis Krompaß. FUSION 2016.
• Deriving validity time in knowledge graph
  • Julien Leblay and Melisachew Wudage Chekol. WWW 2018.
• HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding
  • Shib Sankar Dasgupta, Swayambhu Nath Ray, Partha Talukdar. EMNLP 2018.
  • Code (TF based)

Dynamic Graph Embedding

• Representation Learning over Dynamic Graphs
  • Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha. ArXiv.
  • DyREP: Learning Representations over Dynamic Graphs
• DynGEM: Deep Embedding Method for Dynamic Graphs
  • Palash Goyal, Nitin Kamra, Xinran He, Yan Liu. ArXiv.
• Graph2Seq: Scalable Learning Dynamics for Graphs
  • Anonymous, under review at ICLR 2019.
• Dynamic Graph Representation Learning via Self-Attention Networks
  • Anonymous, under review at ICLR 2019.
• Continuous-Time Dynamic Network Embeddings
  • 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
  • Jinyin Chen, Xuanheng Xu, Yangyang Wu, Haibin Zheng
• Learning Dynamic Embeddings from Temporal Interaction Networks
  • Srijan Kumar, Xikun Zhang, Jure Leskovec
• Dynamic Graph Convolutional Networks
  • Franco Manessi, Alessandro Rozza, Mario Manzo
• Streaming Graph Neural Networks
  • Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin
• dynnode2vec: Scalable Dynamic Network Embedding
  • Sedigheh Mahdavi, Shima Khoshraftar, Aijun An
• Dynamic Network Embedding:An Extended Approach for Skip-gram based Network Embedding
  • Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang

Knowledge Graph Embedding

• Modeling Relational Data with Graph Convolutional Networks
  • Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling. ESWC 2018.
  • Code (Keras based), Code (TF based)
• Neural Relational Inference for Interacting Systems
  • Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel. ICML 2018.
  • Code (Pytorch based)
• Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs
  • Daniel Neil, Joss Briody, Alix Lacoste, Aaron Sim, Paidi Creed, Amir Saffari

Static Graph Embedding

• Inductive Representation Learning on Large Graphs
  • William L. Hamilton, Rex Ying, Jure Leskovec
  • Code (TF based), Code (Pytorch based)
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
  • Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L. Hamilton, Jure Leskovec
• Stochastic Training of Graph Convolutional Networks with Variance Reduction
  • Jianfei Chen, Jun Zhu, Le Song
• A Higher-Order Graph Convolutional Layer
  • Sami Abu-El-Haija, Nazanin Alipourfard, Hrayr Harutyunyan, Amol Kapoor, Bryan Perozzi
• Higher-order Graph Convolutional Networks
  • John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, and Anup Rao

Survey

• Deep Learning on Graphs: A Survey
  • Ziwei Zhang, Peng Cui, Wenwu Zhu
• Graph Neural Networks: A Review of Methods and Applications
  • Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun
• A Comprehensive Survey on Graph Neural Networks
  • Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu
• A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications
  • Hongyun Cai, Vincent W. Zheng, Kevin Chen-Chuan Chang
• How Powerful are Graph Neural Networks?
  • Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka. ICLR 2019.

Others

• Temporal Convolutional Networks: A Unified Approach to Action Segmentation
  • Colin Lea, Rene Vidal, Austin Reiter, Gregory D. Hager
• What to Do Next: Modeling User Behaviors by Time-LSTM
  • Yu Zhu, Hao Li, Yikang Liao, Beidou Wang, Ziyu Guan, Haifeng Liu, Deng Cai. IJCAI 2017.
• Patient Subtyping via Time-Aware LSTM Networks
  • Inci M. Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K. Jain, Jiayu Zhou. KDD 2017.

Useful Libararies

• Deep graph library