Layers for GNNs

 

Layers for GNNs

  • combined messages from adjacent nodes by summing them together with the transformed current node post-multiplying the node embedding matrix H by the adjacency matrix plus the identity A + I

Combining Current Node and Aggregated Neighbors

Residual Connections

Mean Aggregation

  • Sometimes it’s better to take the average of the neighbors rather than the sum; this can be superior if the embedding information is more important and the structural information less so since the magnitude of the neighborhood contributions will not depend on the number of neighbors

Kipf Normalization

Max Pooling Aggregation

Aggregation by Attention