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Incorporating high-frequency information into edge convolution for link prediction in complex networks.

Zhiwei Zhang1, Haifeng Xu2, Guangliang Zhu2

  • 1School of Informatics and Engineering, Suzhou University, Suzhou, 234000, China. zzwloveai@gmail.com.

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This study introduces EdgeConvHiF, a novel graph neural network for link prediction in complex networks. By integrating high-frequency node information, it overcomes limitations of existing models, improving prediction accuracy and stability.

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Area of Science:

  • Graph Neural Networks
  • Complex Networks
  • Machine Learning

Background:

  • Link prediction is crucial for recommendation systems, knowledge graphs, and biomedical research.
  • Current graph neural networks often neglect high-frequency node information, leading to over-smoothing and reduced performance.
  • This limits the effectiveness of existing models in accurately predicting links.

Purpose of the Study:

  • To propose a novel edge convolutional graph neural network, EdgeConvHiF, for enhanced link prediction.
  • To address the over-smoothing issue in graph neural networks by incorporating high-frequency node information.
  • To improve the accuracy and stability of link prediction in complex networks.

Main Methods:

  • Developed EdgeConvHiF, an edge convolutional graph neural network model.
  • Integrated high-frequency node information into the representation learning process.
  • Performed link prediction through link classification.

Main Results:

  • EdgeConvHiF demonstrated high stability across experiments.
  • The proposed model outperformed existing representative baselines in link prediction tasks.
  • Validated through extensive experiments on real-world network benchmarks.

Conclusions:

  • EdgeConvHiF effectively fuses high-frequency node information for superior link prediction.
  • The model offers a significant advancement over current graph neural network approaches.
  • EdgeConvHiF provides a stable and advantageous solution for link prediction in complex networks.