Heterogeneous Graph

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

Models that define an energy landscape rather than explicit probabilities.

Probabilistic energy-based neural network with hidden variables.

Simplified Boltzmann Machine with bipartite structure.

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

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

Extension of convolution to graph domains using adjacency structure.

GNN using attention to weight neighbor contributions dynamically.