Junkai Huang

Bridging Feature-structural Homophily and Long-range Heterogeneity for Self-supervised Heterogeneous Graph Learning

This work proposes a self-expressive solver that captures the complementary homophily between meta-paths and node features to obtain ho-mophilous representations and designs separate path encoders to model diverse interactions, thus explicitly including cross-type interactions while mitigating noise via adaptive fusion.