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.
Minda Chen, Yujie Mo, Junkai Huang et al.
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