Edge-Induced Subgraph Representation Learning
A variety of approaches have been proposed for subgraph-level representation learning. However, these approaches have primarily been developed and evaluated under node-induced subgraph settings, where each subgraph is defined by a selected set of nodes. In contrast, subgraph prediction tasks in which subgraphs are induced by selected sets of edges remain largely unexplored, despite arising naturally in domains such as knowledge graph reasoning, scene graph understanding, and functional connectivity analysis in network neuroscience. Edge-induced subgraph prediction introduces two technical requirements beyond those of the node-induced setting: (1) sensitivity to subgraph-internal edge structure and (2) isolation of subgraph-specific information within a mini-batch. To address these requirements, we introduce the segregated graph, a construction that represents the internal structure of each subgraph via subgraph-specific copies of base-graph nodes connected only by the edges selected for that subgraph. We perform message passing in parallel on the segregated graph and the base graph, and fuse the resulting representations at each layer through identity-based mixing, thereby combining internal-structure awareness with boundary and global contextual information. Experiments on three benchmarks derived from DocRED, Visual Genome, and the Human Connectome demonstrate that our method consistently outperforms existing subgraph prediction approaches, confirming the effectiveness of jointly modeling the segregated graph and the base graph.