GraphSAGE does: samples and aggregates a fixed-size neighborhood

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

Nodes in GNNs aggregate features from neighbors

Graph neural network

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

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

Why can't we find our friends easily as we move to a city with more and more neighborhoods?

cosine similarity is preferred over dot product for normalized embeddings

Why do we need a special way to measure similarity in high-dimensional spaces?

t-SNE preserves local structure

Can we see the hidden patterns in a cloud of data points?