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  • 文竹,袁立宁,黄伟,黄琬雁,莫嘉颖,冯文刚.基于图多层感知机的节点分类算法[J].广西科学,2023,30(5):942-950.    [点击复制]
  • WEN Zhu,YUAN Lining,HUANG Wei,HUANG Wanyan,MO Jiaying,FENG Wengang.Node Classification Based on Graph Multi-Layer Perceptron[J].Guangxi Sciences,2023,30(5):942-950.   [点击复制]
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基于图多层感知机的节点分类算法
文竹1, 袁立宁1,2, 黄伟3, 黄琬雁1, 莫嘉颖1, 冯文刚2
0
(1.广西警察学院信息技术学院, 广西南宁 530028;2.中国人民公安大学国家安全学院, 北京 100038;3.南宁职业技术学院人工智能学院, 广西南宁 530008)
摘要:
多数图神经网络(Graph Neural Networks,GNN)通过设计复杂的节点信息传递和聚合方式,以提升节点分类等图分析任务的实验表现,而本文提出了一种无需信息传递和聚合的图多层感知机(Multi-Layer Perceptron,MLP)模型A&T-MLP,利用属性和拓扑信息引导的对比损失来增强模型表征能力。A&T-MLP首先使用属性矩阵和邻接矩阵计算节点间的属性和拓扑相似度;然后使用基于相似度信息引导的对比损失,增大特征空间中相似节点的一致性和不相似节点的差异性;最后构建多层感知机模型并引入交叉熵损失进行端到端训练。在节点分类任务中,A&T-MLP表现优于基线模型,Wikipedia数据集上的Micro-F1和Macro-F1相较GNN模型图卷积网络(Graph Convolutional Network,GCN)提升了15.86%和13.64%。实验结果表明,A&T-MLP能够通过对比损失保留丰富原始图的信息,提升模型性能。此外,A&T-MLP在处理拓扑信息不准确的图数据时具有较为明显的优势,即使在缺失80%拓扑信息的极端情况下,其实验表现依然优于基线模型。
关键词:  图神经网络  多层感知机  节点属性  对比学习  节点分类
DOI:10.13656/j.cnki.gxkx.20231121.013
投稿时间:2023-09-08修订日期:2023-10-19
基金项目:广西哲学社会科学规划研究课题(21FGL027),广西法学会法学研究课题(GFKT2023-C3),广西警察学院校级科研项目(2022KYZ17)和中央高校基本科研业务费专项资金项目(2022JKF02002)资助。
Node Classification Based on Graph Multi-Layer Perceptron
WEN Zhu1, YUAN Lining1,2, HUANG Wei3, HUANG Wanyan1, MO Jiaying1, FENG Wengang2
(1.School of Information Technology, Guangxi Police College, Nanning, Guangxi, 530028, China;2.School of National Security, People's Public Security University of China, Beijing, 100038, China;3.School of Artificial Intelligence, Nanning College for Vocational Technology, Nanning, Guangxi, 530008, China)
Abstract:
Many Graph Neural Networks (GNN) improve the performance of graph analysis tasks,such as node classification,by designing efficient information propagation and aggregation methods.However,this paper proposes a graph Multi-Layer Perceptron (MLP) model A&T-MLP does not rely on information propagation and aggregation.It enhances its representation ability using through the utilization of contrastive loss guided by attribute and topology information.Firstly,attribute matrix and adjacency matrix were are used to calculate the attribute and topology similarity between nodes.Secondly,the contrastive loss guided by similarity information is applied to increase the consistency of similar nodes and the difference of dissimilar nodes in feature space.Thirdly,the multi-layer perceptron is constructed,and the end-to-end training process incorporates the cross-entropy loss function.A&T-MLP outperforms the baselines in the node classification.Micro-F1 and Macro-F1 increased by 15.86% and 13.64% respectively,compared to the GNN model Graph Convolutional Network (GCN) on Wikipedia dataset.The results show that A&T-MLP effectively preserves richer graph information,leading to improved performance.Besides,the proposed method exhibits notable advantages in inaccurate topology.Even in the extreme case where 80% of edges are missing, A&T-MLP can still superior against baselines.
Key words:  graph neural network  multi-layer perceptron  node attributes  contrastive learning  node classification

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