EasyDeepLearn

Modern graph representation learning — GNNs.

hard

Answer

  • Graph Neural Networks aggregate neighbor features iteratively: hvl+1  =  UPDATEh_{v}^{l + 1}\; = \;\mathrm{UPDATE}(hvlh_{v}^{l}, AGG(hul    u  ∈  N(v))\mathrm{AGG}(h_{u}^{l}\;\;u\; \in \;N(v))).
  • Variants: GraphSAGE (sampled aggregation), GAT (attention weights), GCN (spectral), MPNN (message passing).
  • Trained supervised, semi-sup, or self-supervised (GraphCL, BGRL).
  • Uses: recommendation, drug discovery, molecule property prediction, fraud rings.
Check yourself — multiple choice
  • Random
  • Iteratively aggregate neighbor features hvl+1  =  UPDATE(hvl,  AGG(hu))h_{v}^{l + 1}\; = \;\mathrm{UPDATE}(h_{v}^{l}, \;\mathrm{AGG}(h_{u})); GraphSAGE / GAT / GCN / MPNN; used in recsys, drug discovery, fraud rings
  • Same as CNN
  • Not real

GNN: iterative neighbor aggregation; GraphSAGE / GAT / GCN / MPNN.

#representation-learning#deep-learning

Practise Unsupervised Learning

214 interview questions in this topic.

Read the lessonFlashcardsTake the quiz

Related questions