Subgraph Representation Learning on Heterogeneous Graphs

Subgraph representation learning on heterogeneous graphs requires accounting jointly for subgraph membership and the types of nodes and relations in a shared base graph. Existing approaches primarily address node- or graph-level heterogeneity, leaving subgraph-level challenges less explored. We propose Cross-Type Anchor Projection (CTAP), which decomposes the target subgraphs and the base graph by node type, representing each type-specific node set within its surrounding graph. Cross-type projection maps each other-type node (an anchor) to the subgraph of its neighbors, yielding views that relate these sets to anchors and capture long-range dependencies. Semantic fusion combines representations learned on the views, while membership pooling incorporates cross-type neighbor and constituent-node information before the final fusion across node types. Among the compared methods, CTAP achieves the highest mean test micro-F1 on five of six new datasets drawn from team discovery, rare-disease diagnosis, and gene/protein set analysis.