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Related Experiment Video

Updated: Jan 29, 2026

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A Multi-Branch Training Strategy for Enhancing Neighborhood Signals in GNNs for Community Detection.

Yuning Guo1, Qiang Wu2, Linyuan Lü3

  • 1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, China.

Entropy (Basel, Switzerland)
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

Graph neural networks (GNNs) struggle with community detection due to oversmoothing. This study introduces a novel training strategy to enhance local signals, improving community identification in complex networks.

Keywords:
community detectiongradient modulationmulti-branchneighborhood information

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Area of Science:

  • Graph Neural Networks
  • Network Science
  • Machine Learning

Background:

  • Community detection in complex networks is crucial.
  • Graph Neural Networks (GNNs) are the mainstream approach.
  • GNNs suffer from Laplacian oversmoothing, diluting vital neighborhood signals.

Purpose of the Study:

  • To propose a novel training strategy to strengthen local signals for community detection.
  • To address the contradiction caused by GNN oversmoothing in community identification tasks.

Main Methods:

  • Designed a multi-branch learning structure to inject gradients during backpropagation.
  • Modulated gradients with GNN's message-passing path to supplement initial layers with first-order topological information.
  • Constructed the Network Structure-Informed GNN (NIGNN).

Main Results:

  • Achieved 0.6-3.6% improvement in community detection tasks compared to basic models.
  • Demonstrated good performance in t-tests.
  • Showed strong robustness in networks with incomplete information.

Conclusions:

  • The proposed method effectively preserves core local information in deep GNNs.
  • The NIGNN framework offers a novel solution for enhancing community detection.
  • The approach is broadly applicable to various GNN architectures (GCN, GAT, GraphSAGE).