Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (2): 107-115.doi: 10.11896/jsjkx.240600091

• Database & Big Data & Data Science • Previous Articles     Next Articles

Node Classification Algorithm Fusing High-order Group Structure Information

ZHENG Wenping1,2,3, HAN Yiheng1, LIU Meilin1   

  1. 1 School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China
    2 Key Laboratory of Computation Intelligence and Chinese Information Processing of Ministry of Education,Shanxi University,Taiyuan 030006,China
    3 Institute of Intelligent Information Processing,Shanxi University,Taiyuan 030006,China
  • Received:2024-06-17 Revised:2024-09-16 Online:2025-02-15 Published:2025-02-17
  • About author:ZHENG Wenping,born in 1979.Ph.D,professor,Ph.D supervisor,is a member of CCF(No.22709M).Her main research interests include complex network analysis,bioinformatics,etc.
  • Supported by:
    National Natural Science Foundation of China(62072292),1331 Engineering Project of Shanxi Province,China and Ministry of Education Industry-University Cooperation and Collaborative Education Project,China(220902842025336).

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Abstract: There are usually high-order group structures with specific local connection patterns in local neighborhood of nodes,which can more accurately describe the topological characteristics of nodes and help to understand the structural characteristics of the network and the interaction patterns between nodes.The structural similarity between nodes can be computed using high-order group structural features within the local neighbors of a node,and a node classification algorithm is proposed based on fusing high-order group structure information(NHGS).Weisfeiler-Lehman(WL) algorithm is used to iteratively aggregate the label information of the k-tuple in a node's neighborhood to update its k-tuple label.The number of occurrences of nodes in different k-tuple labels constitutes the feature vector of nodes,and the cosine similarity between feature vectors is used to represent the structural similarity between nodes.Combined with the attribute information of the nodes,the node embedding is obtained through the autoencoder neural network,and then the nodes in the network are classified.NHGS combines the k-tuple node group structure information with the attribute information of a node to obtain the node representation containing the high-order structure information.Experiments on real attribute networks show that the proposed method can effectively calculate the structural similarity between nodes,and improve the performance of node classification tasks.

Key words: Node classification, High-order structure, Structural similarity, Network representation, Graph neural network

CLC Number: 

  • TP391
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