March 24, 2024Open Access

Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks

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CQChenyang QiuBeijing University of Posts and Telecommunications GNGuoshun NanBeijing University of Posts and Telecommunications TXTianyu XiongCapital University

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Implication

Benchmark evaluation demonstrates improved node classification robustness in heterophilic graphs, highlighting effective mitigation of structural out-of-distribution threats.

Key Points

Automated recovery of latent homophilic structures enhances graph convolution networks against structural out-of-distribution vulnerabilities across complex heterophilic graphs.
Benchmark evaluation utilizes multi-node interactions alongside pairwisely constrained contrastive learning, iteratively refining node representations for node classification.
Mitigating structural distribution shifts in heterophilic graphs enables dependable spatial data mining, establishing a robust foundation for diverse topological learning tasks.

Cite This Study

Qiu et al. (2024) studied this question.

synapsesocial.com/papers/68e72962b6db6435876a30ca https://doi.org/10.1609/aaai.v38i8.28741

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Implication

Benchmark evaluation demonstrates improved node classification robustness in heterophilic graphs, highlighting effective mitigation of structural out-of-distribution threats.

Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks

Discussion

Member takes

Implication

Key Points

Cite This Study

Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks

Discussion

Member takes

Implication

Key Points

Cite This Study

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