One Interaction, Two Consequences: Rethinking the Computational Role of Edges in Graph Learning

Graph neural networks (GNNs) are widely used to learn from graphs, and most of their designs focus on what information a node should receive. In real graph data, however, an edge usually records an interaction, such as a paper written by a student and a professor, and the same interaction can mean different things to the two participants. Such designs use an edge only to shape the message sent to one receiving node and do not model the interaction as one fact with a consequence for each participant. Graphs that differ in homophily, heterogeneity, and directionality have been handled by separate specialized GNNs that fix the meaning of an edge in advance. We instead make the interaction itself the unit of computation and propose a simple model, Learning from Interaction Consequences (LINC). LINC forms one representation of an interaction from both participants, and the two participants use this representation through scales computed from their own embeddings. As a result, one interaction has two consequences, one for each participant. Since a consequence also carries the other participant's features, ordinary message passing is a special case of LINC. We prove that the interaction representation of an undirected edge does not depend on the order of the participants, while the two consequences can still differ. On 16 benchmarks covering homophilic, heterophilic, heterogeneous, and directed graphs, LINC uses one architecture throughout and achieves the best average rank in every group against 38 baselines. In ablation studies, the interaction representation alone carries most of what a node needs from its neighbors. Different consequences for the two participants help on most graphs, especially on directed and heterophilic graphs, where the participants often differ in role or label.