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  1. Home
  3. Local And High-order Consistency Coding And Adaptation For Cross-hypergraph Node Classification.
  1. Home
  3. Local And High-order Consistency Coding And Adaptation For Cross-hypergraph Node Classification.

Related Experiment Video

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

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Local and High-Order Consistency Coding and Adaptation for Cross-Hypergraph Node Classification.

Hanrui Wu, Yanxin Wu, Lei Tian

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |April 9, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces a new method for node classification in hypergraphs, addressing challenges in data labeling and limited use of high-order information. The approach effectively transfers knowledge between hypergraphs for improved classification accuracy.

    Related Experiment Videos

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.4K

    Area of Science:

    • Hypergraph learning
    • Machine learning
    • Data science

    Background:

    • Node classification is crucial in hypergraph learning, but acquiring labeled data is difficult in new hypergraphs.
    • Existing methods often overlook high-order relationships, limiting representation discrimination.

    Purpose of the Study:

    • To address challenges in cross-hypergraph node classification by leveraging knowledge from a well-labeled source hypergraph to a target hypergraph.
    • To develop a model that learns both discriminative and transferable node representations.

    Main Methods:

    • Proposes Local and High-order Consistency Coding and Adaptation (LHCCA) model.
    • Exploits local and high-order consistency relationships within each hypergraph.
    • Employs attention mechanisms for unified representations and adversarial domain adaptation with contrastive learning for feature transfer.

    Main Results:

    • LHCCA learns discriminative and transferable node representations.
    • The model effectively utilizes both local and high-order consistency information.
    • Extensive experiments show the proposed model's effectiveness on real-world datasets.

    Conclusions:

    • The LHCCA model offers an effective solution for cross-hypergraph node classification.
    • It successfully addresses limitations of existing methods by incorporating high-order information and enabling knowledge transfer.
    • Theoretical analyses support the model's desirable properties and practical performance.