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