This work proposes HAAM, an adaptive node classification approach for multiplex graphs that models per-dimension degrees of homophily and heterophily through dimension-specific compatibility matrices, and shows that HAAM achieves competitive performance compared to representative baseline methods.
It is demonstrated that the information needed to classify heterophilic nodes correctly is often recoverable by retraining the classification head of a model, or even just the final linear classification layer.
Preben M. Ness, Fariz Ikhwantri, D. Marijan· 0 citations
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...
Min-Da Chen, Yujie Mo, Jun-Kai Huang et al.· Proceedings of the Thirty-Fi...· 0 citations
HeAD-CP is proposed, a family of node-wise diffusion variants whose coefficients are determined by a label-free local-homophily estimate derived from the GNN softmax, which are most effective at extreme heterophily, intermediate heterophily, and moderate-to-high homophily, respectively, and all preserve the marginal co...
A new descriptive homophily measure for general social hypergraphs where group interactions involve arbitrary number of individuals is proposed, and constraints of monotonic and majority homophily for two-class labels are established, providing a framework for analyzing homophily patterns.
Yunping Wang, Zhiheng Zhou, Mingwei Li et al.· Chaos· 0 citations
Graph Neural Networks (GNNs) have exhibited extraordinary performance in data mining. The success depends on the message passing mechanism, which is consistent with the homophily assumption that adjacent nodes share similar behaviors. However, the connected nodes in real-world graphs often show dissimilar behaviors, wh...
Wenjun Wang, Xin Cao, Yawen Li et al.· Proceedings of the 32nd ACM...· 0 citations
This study introduces a new ranking framework that integrates a quasi-Laplacian structural measure with a gravity-inspired aggregation process and demonstrates that the proposed framework consistently outperforms existing techniques in terms of accuracy, resolution, and computational simplicity.
Shima Esfandiari, S. M. Fakhrahmad· arXiv.org· 0 citations
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