Graph Convolution

GNN using attention to weight neighbor contributions dynamically.

Graphs containing multiple node or edge types with different semantics.

Structured graph encoding facts as entity–relation–entity triples.

Models that define an energy landscape rather than explicit probabilities.

Neural networks that operate on graph-structured data by propagating information along edges.

GNN framework where nodes iteratively exchange and aggregate messages from neighbors.