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0:00 Introduction
0:23 Graphs are everywhere
0:42 Graph Neural Networks
1:10 Problem: Many Graphs are Dynan
3:48 From Static to Dynamic Grapl
7:24 CTDGS: Many Types of Event
8:22 Why is Learning on Dynamic Graphs Different? Model needs to: - Handle different types of events - Use the time information of the events - Efficiently and incrementally incorporate new events - Different tasks: predict when something will happen
10:08 Temporal Graph Model
11:06 Encoding a Temporal Graph
17:31 TGN: Temporal Graph Networ
20:04 Modules: Message Function - Each event generates a message • Messages will be used to update the memory
21:17 Modules: Memory Updater
21:47 Modules: Graph Embedding
23:38 Future Link Prediction
24:23 Scalability
25:10 Experiments: Future Edge Predicti
26:35 Experiments: Dynamic Node Classificati
27:44 Predicting when events will happen - Qualitatively different question from other tasks - A decoder which makes use of Temporal Point Processes is needed
28:28 Future Work
29:10 Conclusion
29:35 Questions?
32:34 Ablation Study (Future edge prediction)
