GCN (Graph Convolutional Network) does: spectral convolution approximated by neighbor averaging

GAT (Graph Attention Network) adds: learned attention weights between neighbors

GAT adds learned attention weights between neighbors, enhancing node feature aggregation

the over-smoothing problem is in GNNs: deep GNNs make all node features converge

Over-smoothing in GNNs: Deeper layers cause node features to converge too much, losing unique node identities

message passing does in GNNs: each node aggregates features from its neighbors

Each node aggregates features from its neighbors using message passing

Graph neural network

Graph pooling reduces graphs to single vectors for graph-level prediction

GraphSAGE does: samples and aggregates a fixed-size neighborhood

GraphSAGE samples and aggregates a fixed-size neighborhood

batch size affects generalization: larger batches find sharper minima

Larger batch sizes lead to sharper minima, enhancing generalization by providing more accurate gradient estimates