EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graph Representation Learning, Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao Schardl, Charles Leiserson, 2020Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34 (Association for the Advancement of Artificial Intelligence)DOI: 10.1609/aaai.v34i04.5984 - Introduces a method to dynamically update GNN parameters over time using an RNN, specifically for discrete-time dynamic graphs.
Temporal Graph Networks for Deep Learning on Dynamic Graphs, Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, Michael Bronstein, 2020Advances in Neural Information Processing Systems (NeurIPS), Vol. 33DOI: 10.48550/arXiv.2006.10637 - Introduces the Temporal Graph Network (TGN) framework, a memory-based approach for continuous-time dynamic graphs.