Temporal Graph Networks - GNN Paper Explained

Dive deep into the world of Temporal Graph Networks (TGN) with this comprehensive video explanation. Explore dynamic graphs, learn how to obtain vectorized representations of time, and uncover the intricate details behind the TGN paper. Gain insights into suboptimal strategies, temporal neighborhoods, and the high-level system overview. Discover solutions to information leakage, understand main modules, and tackle the memory staleness problem. Delve into temporal graph attention, vector representation of time, and batch size tradeoffs. Analyze results, ablation studies, and recap the entire system. Address confusing aspects and enhance your understanding of this advanced topic in graph machine learning.

Dynamic graphs
Suboptimal strategies
Terminology, temporal neighborhood
High-level overview of the system
We need to go deeper
Using temporal information to sample
Information leakage and the solution
Main modules explained
Memory staleness problem
Temporal graph attention
Vector representation of time
Batch size tradeoff
Results and ablation studies
Recap of the system
Some confusing parts