Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (8): 304-312.doi: 10.11896/jsjkx.240100139

• Artificial Intelligence • Previous Articles     Next Articles

Multi-channel Graph Convolutional Networks Enhanced by Label Propagation Algorithm

YUAN Lining1,2, FENG Wengang1, LIU Zhao3   

  1. 1 School of National Security,People’s Public Security University of China,Beijing 100038,China
    2 School of Public Security Big Data Modern Industry,Guangxi Police College,Nanning 530028,China
    3 Graduate School,People’s Public Security University of China,Beijing 100038,China
  • Received:2024-01-17 Revised:2024-05-07 Online:2024-08-15 Published:2024-08-13
  • About author:YUAN Lining,born in 1995,doctoral student,is a member of CCF(No.H5844M).His main research interests include machine learning and graph neural network.
    FENG Wengang,born in 1982,Ph.D,professor,Ph.D supervisor.His main research interests include pattern recognition and public security intelligence.
  • Supported by:
    National Key Research and Development Program of China(2023YFC3321604),Social Science Fund of Guangxi(23FTQ005),Social Science Fund of Beijing(22GLB225) and Special Fund of Guangxi Public Security Department(2023GAQN092).

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Abstract: Most graph convolutional networks(GCN) improve the experimental performance of node classification tasks by designing efficient methods for information propagation and preservation,while ignoring the propagation of node label information in the topological and attribute spaces.Aiming at the above problems,the paper proposes a multi-channel graph convolution model MGCN-LPA enhanced by the label propagation algorithm(LPA).The model enhances the propagation of node features and label information by increasing the weights of relationship between nodes of the same class in the attribute space and topology space.Firstly,it calculates the similarity values of different node attributes and generates an attribute relation graph using the k-nearest neighbor algorithm.Then,it combines the GCN and LPA in the graph convolution layer GCN-LPA to extract potential features from the attribute graph and attribute relation graph,generating topological node representations and attribute node representations.Finally,the method combines the topological and attribute representations and utilizes the final representation for node classification tasks.On three benchmark graph datasets,the experimental performance of MGCN-LPA can match the current state-of-the-art baseline models.The classification results on the Cora and Citeseer datasets show improvements of 9.3% and 12% respectively compared to the best-performing baseline.The experimental results demonstrate that MGCN-LPA can increase the weights of paths between nodes of the same class and enhance the propagation of information among nodes of the same class,thereby enhancing the performance of node classification tasks.In addition,the ablation experiments demonstrate that the fusion of both topological space and attribute space information in MGCN-LPA enhances the model’s representational capacity and ge-neralization compared to variants using only one type of information.This fusion allows for the full extraction and preservation of latent features present in the original graph.

Key words: Graph convolutional network, Label propagation algorithm, Attribute graph, Attribute relation graph, Node classification

CLC Number: 

  • TP183
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