融合多重特征的噪声网络对齐方法  

Noise network alignment method integrating multiple features

作　　者：咸宁 范意兴 廉涛 郭嘉丰[1,2] XIAN Ning;FAN Yixing;LIAN Tao;GUO Jiafeng(Key Laboratory of Network Data Science and Technology,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China;School of Computer Science and Technology,University of Chinese Academy of Sciences,Beijing 100190,China;College of Computer Science and Technology(College of Data Science),Taiyuan University of Technology,Jinzhong 030600,Shanxi,China)

机构地区：[1]中国科学院计算技术研究所网络与数据科学与技术重点实验室,北京100190 [2]中国科学院大学计算机科学与技术学院,北京100190 [3]太原理工大学计算机科学与技术学院(大数据学院),山西晋中030600

出　　处：《山东大学学报（理学版）》2024年第7期64-75,共12页Journal of Shandong University(Natural Science)

基　　金：国家自然科学基金资助项目(62372431);国家重点研发计划项目(2021QY1701,2023YFA1011602);中国科学院青年创新促进会会员项目(2021100);中国科学院计算技术研究所创新项目(E261090);国防科技创新项目。

摘　　要：针对网络对齐任务中网络结构差异大和锚节点对噪声大的问题,提出一种基于多轮迭代的网络对齐方法。该方法在每轮迭代时使用多种启发式方法计算不同维度的节点特征,利用多重特征的组合来评估锚节点的可靠性,过滤其中潜在的噪声,增强每轮对齐过程的置信度;使用图神经网络增强无属性节点之间的一致性,减轻网络结构差异带来的影响。实验结果表明,该方法可以在高噪声的情况下具有高准确率,验证了其有效性。A multi-round iterative network alignment method is proposed to address the challenges of large structural differences and high noise sensitivity in anchor nodes in network alignment tasks.The method calculates node features of different dimensions using various heuristic approaches at each iteration,utilizing the combination of multiple features to assess the reliability of anchor nodes,filter potential noise,and enhance the confidence of each alignment round.Additionally,a graph neural network is employed to improve the consistency between nodes without attributes,mitigating the impact of structural differences in networks.Experimental results demonstrate that this method achieves high accuracy under high noise conditions,verifying its effectiveness.

关 键 词：网络对齐 图同构网络 噪声过滤 图元 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]