[Day 97] Review of the GNN structure and training (last 2 days) + starting Colab 2 of XCS224W: ML with Graphs
Hello :) Today is Day 97! A quick summary of today: read through and reviewed my notes on intro to GNNs (Day 94) read through and reviewed my notes on Designing a GNN layer (Day 95) read through and reviewed my notes on GNN training (Day 96) started 2nd homework of the course covering the above two First I had a quick look through the 2nd homework that covers material covered in Day 94 and Day 95, and I decided that it would be best to do a proper review of the important concepts from those days Topics I went over: Designing a single layer of a GNN, message computation, aggregation, GCN, GraphSAGE, GAT, attention and multi-head attention in graphs, stacking GNN layers, the problem of over-smoothing, shallow GNNs, using skip connections graph augmentation, feature augmentation, training GNNs on a node-level, edge-level and graph-level, pooling for graph-level tasks, DiffPool, supervised, unsupervised and self-supervised learning, loss functions, evaluation metrics,