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

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

• Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs
  • 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

• Learning Sequence Encoders for Temporal Knowledge Graph Completion (Interpolation)
  • Alberto Garcia-Duran, Sebastijan Dumancic, Mathias Niepert. EMNLP 2018.
• Towards time-aware knowledge graph completion (Interpolation)
  • Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Baobao Chang, Sujian Li and Zhifang Sui. COLING 2016.
• Deriving validity time in knowledge graph (Interpolation)
  • Julien Leblay and Melisachew Wudage Chekol. WWW Workshop 2018.
• HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding (Interpolation)
  • Shib Sankar Dasgupta, Swayambhu Nath Ray, Partha Talukdar. EMNLP 2018.
  • Code (TF based)
• Predicting the co-evolution of event and knowledge graphs
  • Cristóbal Esteban, Volker Tresp, Yinchong Yang, Stephan Baier, Denis Krompaß. FUSION 2016.
• Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
• Diachronic Embedding for Temporal Knowledge Graph Completion
• Hybrid-TE: Hybrid Translation-based Temporal Knowledge Graph Embedding
• Tensor Decompositions for Temporal Knowledge Base Completion

• DyREP: Learning Representations over Dynamic Graphs (Extrapolation)
  • Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, Hongyuan Zha. ICLR 2019.
• DynGEM: Deep Embedding Method for Dynamic Graphs
  • Palash Goyal, Nitin Kamra, Xinran He, Yan Liu. IJCAI 2017.
• Graph2Seq: Scalable Learning Dynamics for Graphs
  • Shaileshh Bojja Venkatakrishnan, Mohammad Alizadeh, Pramod Viswanath
• Dynamic Graph Representation Learning via Self-Attention Networks
  • Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, Hao Yang
• 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

• Dynamic Network Embedding: An Extended Approach for Skip-gram based Network Embedding
  • Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang
• EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
  • 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
  • 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
  • Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, Xavier Bresson, ICONIP 2017.
• Dynamic Network Embedding by Modeling Triadic Closure Process
  • Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang. AAAI 2018.
• DynGAN: Generative Adversarial Networks for Dynamic Network Embedding
  • Ayush Maheshwari, Ayush Goyal, Manjesh Kumar Hanawal, Ganesh Ramakrishnan. NeurIPS 2019 Workshop.

• 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. ICONIP 2017.

• 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

• 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.
• Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey
  • Seyed Mehran Kazemi, Rishab Goel, Kshitij Jain, Ivan Kobyzev, Akshay Sethi, Peter Forsyth, Pascal Poupart

• 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.

• Deep graph library
• Pytorch geometric