TGN: Temporal Graph Networks for Dynamic Graphs
Video for MLSS 2020 Tübingen presenting TGN: Temporal Graph Networks for Dynamic Graphs.
TGN: Key Insights
- Memory: to store the dynamic state of each node. It’s updated each time a node is involved in an interaction.
- Graph Embedding: when computing the temporal embedding for a node, aggregate the memories of the neighbors.
TGN: Modules and Training
Computations performed by TGN on a batch of time-stamped interactions.
Flow of operations of TGN used to train the memory-related modules.
- Dynamic graphs are very common, but have received little attention so far.
- We propose TGN, which generalizes existing models and achieves SOTA results on a variety of benchmarks.
- The ablation study shows the importance of the different modules.