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

• Temporal knowledge graph completion

  • Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
    • Rakshit Trivedi, Hanjun Dai, Yichen Wang, Le Song. ICML 2017.
  • Learning Sequence Encoders for Temporal Knowledge Graph Completion
    • Alberto Garcia-Duran, Sebastijan Dumancic, Mathias Niepert. ArXiv.
  • 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.

• 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.

• 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.

• Relational inference

  • Neural Relational Inference for Interacting Systems
    • Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, Richard Zemel. ICML 2018.