Knowledge Graph

Models that define an energy landscape rather than explicit probabilities.

Probabilistic energy-based neural network with hidden variables.

Simplified Boltzmann Machine with bipartite structure.

Probabilistic graphical model for structured prediction.

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.

Graphs containing multiple node or edge types with different semantics.