Graph Neural Networks

Graph neural networks (GNNs) are a class of neural network architectures that operate directly on graph-structured data, learning representations of nodes, edges, or entire graphs by propagating information along the topology of the network. Unlike standard neural networks that assume independent and identically distributed samples, GNNs exploit relational structure: a node's representation is computed from its own features and the aggregated features of its neighbors, iteratively, until the network reaches a fixed point. This makes them the natural computational counterpart to the topological constraints of relevance logic: both insist that inference follows the edges of a connection graph, not arbitrary pairwise combination. GNNs have become the dominant architecture for molecular property prediction, social network analysis, and reasoning systems that require relational reasoning over structured knowledge.\n\n\n\n