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基于路径感知邻域的节点分类算法

郑文萍 王晓敏 韩兆荣

数据采集与处理2025,Vol.40Issue(1):134-146,13.
数据采集与处理2025,Vol.40Issue(1):134-146,13.DOI:10.16337/j.1004-9037.2025.01.010

基于路径感知邻域的节点分类算法

Path Connectivity Based Neighbor-Awareness Node Classification Algorithm

郑文萍 1王晓敏 2韩兆荣2

作者信息

  • 1. 山西大学计算机与信息技术学院,太原 030006||计算智能与中文信息处理教育部重点实验室(山西大学),太原 030006||山西大学智能信息处理研究所,太原 030006
  • 2. 山西大学计算机与信息技术学院,太原 030006
  • 折叠

摘要

Abstract

Graph convolutional neural networks obtain the node representation by aggregating the neighbor node information with high similarity,and selecting the appropriate neighborhood for the node and conducting effective aggregation are the keys to the graph convolutional networks.Most of the existing graph convolutional neural networks directly aggregate the node information in the multi-hop neighborhood,without considering the difference of the aggregation weights of different hop neighborhoods on different nodes in the network.Aiming at this,a path connectivity based neighbor-awareness node classification algorithm(PCNA)is proposed.The node neighborhood is determined by the path connectivity information in the network,and the influence weight of different length paths on the similarity calculation between nodes is adaptively perceived to guide the neighborhood aggregation process of graph convolutional neural network.Specifically,PCNA is composed of a neighborhood perceptron and a node classifier.The neighborhood perceptron adaptively obtains the aggregated neighborhood of each node and the influence weights of paths with different lengths based on the reinforcement learning mechanism,and then uses the path connectivity information between nodes to obtain the similarity matrix.The node classifier uses the obtained similarity matrix to perform neighborhood aggregation to obtain node representation and classify nodes.The comparison experiments with 10 classical algorithms on eight real datasets show that the proposed algorithm has better performance in node classification tasks.

关键词

图卷积神经网络/邻域聚合/强化学习/节点相似性/节点分类

Key words

graph convolutional neural networks/neighborhood aggregation/reinforcement learning/node similarity/node classification

分类

信息技术与安全科学

引用本文复制引用

郑文萍,王晓敏,韩兆荣..基于路径感知邻域的节点分类算法[J].数据采集与处理,2025,40(1):134-146,13.

基金项目

国家自然科学基金(62072292) (62072292)

山西省1331工程项目 ()

教育部产学合作协同育人项目(220902842025336). (220902842025336)

数据采集与处理

OA北大核心

1004-9037

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