[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 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, 
  • splitting data


As for assignment two, I am not allowed to share any code from it, but today I did not finish it. I decided to go slowly with this one and completed just 4 of the questions. Tomorrow I think I will be able to do it all and submit to get a final grade ^^


That is all for today!

See you tomorrow :)

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