GraphSAGE does: samples and aggregates a fixed-size neighborhood

non-convex loss landscapes are hard: many local minima and saddle points

Non-convex loss landscapes are hard due to many local minima and saddle points

Graph neural network

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

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

Each node aggregates features from its neighbors using message passing

batch size affects generalization: larger batches find sharper minima

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

the curse of dimensionality makes nearest neighbor search unreliable

High dimensionality dilutes data density, making nearest neighbors less distinct and search unreliable

Graph (abstract data type)

Time complexity of BFS and DFS: O(V + E)