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September 10, 2025IEEE Transactions on Pattern Analysis and Machine IntelligenceOpen Access

HL-HGAT: Heterogeneous Graph Attention Network via Hodge-Laplacian Operator

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Authors

JHJinghan Huang QCQiufeng Chen PZPengli Zhu

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Overview

This framework innovatively incorporates HL filters and simplicial attention pooling to enhance signal representations, suggesting improvements in graph tasks.

Key Points

The HL-HGAT demonstrates enhanced performance in various graph applications, including multi-label and classification challenges.
Utilizing Hodge-Laplacian and simplicial techniques, the model effectively captures complex node relationships within graphs.
The novel approach employs polynomial approximations for computation efficiency, facilitating real-time applications.
HL-HGAT's versatility is confirmed through rigorous evaluation across multiple domains like biology and computer vision.

Cite This Study

Huang et al. (2025) studied this question.

synapsesocial.com/papers/68c1a40f54b1d3bfb60dec17 https://doi.org/10.1109/tpami.2025.3594226

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September 10, 2025IEEE Transactions on Pattern Analysis and Machine IntelligenceOpen Access

HL-HGAT: Heterogeneous Graph Attention Network via Hodge-Laplacian Operator

View Full Paper

Ask AI

Bookmark

Authors

JHJinghan Huang QCQiufeng Chen PZPengli Zhu

Discussion

Member takes

Overview

This framework innovatively incorporates HL filters and simplicial attention pooling to enhance signal representations, suggesting improvements in graph tasks.

Key Points

The HL-HGAT demonstrates enhanced performance in various graph applications, including multi-label and classification challenges.
Utilizing Hodge-Laplacian and simplicial techniques, the model effectively captures complex node relationships within graphs.
The novel approach employs polynomial approximations for computation efficiency, facilitating real-time applications.
HL-HGAT's versatility is confirmed through rigorous evaluation across multiple domains like biology and computer vision.

Cite This Study

Huang et al. (2025) studied this question.

synapsesocial.com/papers/68c1a40f54b1d3bfb60dec17 https://doi.org/10.1109/tpami.2025.3594226

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