8:10

Part 43: edge based graph component pooling

14:59

Part 46: hierarchical graph pooling with structure learning

11:53

Part 45: fast and effective GNN training with linearized random spanning trees

15:18

Part 36: graph pooling for graph neural networks: progress, challenges, and opportunities

23:19

Regime Switching Models with Machine Learning | Piotr Pomorski

12:58

Part 42: grouping-matrix based graph pooling with adaptive number of clusters

10:10

Part 47: graph clustering with graph neural networks

13:23

Part 39: MAXCUTPool: differentiable feature aware maxcut for pooling in graph neural networks