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Updated: Jun 4, 2025

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Simplified PCNet with robustness.

Bingheng Li1, Xuanting Xie1, Haoxiang Lei1

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a simplified and more robust Graph Neural Network (GNN) model, SPC-Net, enhancing graph representation learning across varying homophily levels for improved performance and efficiency.

Keywords:
Adversarial attackGraph filteringHeterophilyPolynomial approximationSpectral method

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Area of Science:

  • Graph Neural Networks
  • Machine Learning
  • Network Science

Background:

  • Graph Neural Networks (GNNs) excel at learning representations for homophilic or heterophilic graphs but struggle with generalization across diverse real-world graph structures.
  • Previous work, Poisson-Charlier Network (PCNet), addressed heterophily but faced limitations in efficacy and efficiency.

Purpose of the Study:

  • To simplify and enhance the robustness of PCNet for improved graph representation learning.
  • To develop a more adaptable and efficient GNN model capable of handling various graph homophily levels.

Main Methods:

  • Extended the filter order to continuous values, reducing model parameters.
  • Introduced two variants with adaptive neighborhood sizes.
  • Conducted theoretical analysis to demonstrate robustness against perturbations and adversarial attacks.

Main Results:

  • The simplified model (SPC-Net) shows enhanced robustness and efficiency compared to PCNet.
  • Validated through semi-supervised learning tasks on diverse homophilic and heterophilic datasets.
  • Achieved improved graph representation learning across different homophily levels.

Conclusions:

  • The proposed SPC-Net offers a more robust and efficient approach to graph representation learning.
  • The model's adaptability and theoretical robustness make it suitable for real-world graph applications.
  • This work advances GNN capabilities in handling complex graph structures with varying homophily.