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Graph convolutional network with tree-guided anisotropic message passing.

Ruixiang Wang1, Yuhu Wang1, Chunxia Zhang2

  • 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a tree-guided anisotropic Graph Convolutional Network (GCN) to improve graph representation learning. The novel method enhances expressiveness and long-range modeling for better performance on complex graph data.

Keywords:
Anisotropic message passingDeep learningGraph convolutional networksGraph structure learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Graph Convolutional Networks (GCNs) traditionally use isotropic aggregation, limiting performance.
  • Existing anisotropic GCNs struggle with expressiveness and long-range dependency modeling.

Purpose of the Study:

  • To propose a novel tree-guided anisotropic GCN for enhanced graph representation learning.
  • To improve expressiveness and long-range modeling capabilities in GCNs.

Main Methods:

  • Decoupled anisotropic aggregation into two stages: path establishment on a tree-like hypergraph and message aggregation with gating.
  • Introduced a novel anisotropic readout mechanism for graph-level feature generation.

Main Results:

  • The proposed model outperforms baseline and recent GCN methods on synthetic and real-world datasets.
  • Ablation studies and theoretical analyses confirm the method's effectiveness.

Conclusions:

  • The tree-guided anisotropic GCN offers superior performance by addressing limitations in aggregation strategies.
  • The model demonstrates significant improvements in graph representation learning for downstream tasks.