南京大学学报（自然科学版）2023，Vol.59Issue(6)：947-960,14.DOI:10.13232/j.cnki.jnju.2023.06.005
利用粗图训练图神经网络实现网络对齐
Training of graph neural networks on coarsening graphs for network alignment
摘要
Abstract
Network alignment is a challenging task that aims to identify equivalent nodes in different networks.Conventional methods face high computational complexity and low accuracy due to the complexity of networks and the lack of supervision.In recent years,Graph Neural Networks(GNN)have been increasingly explored in network alignment algorithms,as they can reduce computational complexity and improve accuracy compared to traditional methods.However,the performance of GNN-based methods is limited by the quality of training data and network size.To address these limitations,we propose a fast and robust unsupervised network alignment method called FAROS.FAROS employs a GNN model trained on coarse graphs for network alignment.The use of coarse graphs for GNN training significantly reduces training data,minimizes weight parameters that must be updated during GNN back-propagation,and accelerates training time.Coarse graphs also mitigate data noise and extract the most important structural features of the network,which facilitates GNN to obtain more resilient embedding vectors.During training,FAROS elevates alignment accuracy by introducing self-supervised learning based on pseudo-anchor node pairs.Experimental results on real datasets demonstrate the effectiveness of FAROS,which is several orders of magnitude faster than comparative methods while maintaining good accuracy.关键词
网络对齐/图神经网络/网络嵌入/粗图/锚节点对Key words
network alignment/graph neural network/network embedding/coarsen graph/anchor node pairs分类
信息技术与安全科学引用本文复制引用
钱峰,张蕾,赵姝,陈洁..利用粗图训练图神经网络实现网络对齐[J].南京大学学报（自然科学版）,2023,59(6):947-960,14.基金项目
国家自然科学基金(61876001),安徽省高校科研计划(2022AH051749),安徽省高校优秀人才支持计划(GXYQ2020054),安徽省高校优秀青年骨干人才国内外访学研修项目(GXGNFX2021148) （61876001）
