International Business Machines Corporation
ADAPTIVE SELF-ADVERSARIAL NEGATIVE SAMPLING FOR GRAPH NEURAL NETWORK TRAINING

Abstract:

A graph neural network (GNN) training method, system, and computer program product in a graph, include generating, by the computing device, one or more one or more hypothetical edges between two or more nodes of a plurality of nodes of a graph neural network, testing, by the computing device, to determine whether the one or more generated hypothetical edges should be connected by using negative sampling, and permanently connecting, by the computing device, the one or more tested hypothetical edges if the negative sampling indicates the connectivity.