May 31, 2024Open Access

Heterophilous Distribution Propagation for Graph Neural Networks

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Authors

ZZZhuonan ZhengZhejiang University of Technology SZSheng ZhouChinese Academy of Sciences HXHongjia XuZhejiang University of Technology

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Implication

Benchmark evaluation demonstrates superior node representation accuracy across heterophilous graphs, highlighting the effectiveness of distribution propagation.

Key Points

Heterophilous distribution propagation improves node representation learning by adaptively separating homophilous and heterophilous graph neighborhoods.
Across 9 benchmark datasets with varying homophily levels, the proposed framework consistently outperforms existing heterophilous neural network models.
Benchmark on 9 benchmark datasets demonstrates that trusted prototype contrastive learning and semantic-aware message passing enable robust graph mining.

Cite This Study

Zheng et al. (2024) studied this question.

synapsesocial.com/papers/68e6785bb6db6435876029b6 https://doi.org/10.48550/arxiv.2405.20640

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Implication

Benchmark evaluation demonstrates superior node representation accuracy across heterophilous graphs, highlighting the effectiveness of distribution propagation.

Heterophilous Distribution Propagation for Graph Neural Networks

Discussion

Member takes

Implication

Key Points

Cite This Study

Heterophilous Distribution Propagation for Graph Neural Networks

Discussion

Member takes

Implication

Key Points

Cite This Study

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