基于社区与社交关系的跨网络身份匹配模型  

A Cross Network Identity Matching Model Based on Community and Social Relationships

作　　者：马廷淮[1] 黄锐 彭可兴 荣欢 MA Ting-huai;HUANG Rui;PENG Ke-xing;RONG Huan(School of Computer Science,Nanjing Unive rsity of Information Science&Technology,Nanjing Jiangsu 210044,China;School of Artificial Intelligence,Nanjing University of Information Science&Techno logy,Nanjing Jiangsu 210044,China)

机构地区：[1]南京信息工程大学计算机学院,江苏南京210044 [2]南京信息工程大学人工智能学院,江苏南京210044

出　　处：《计算机仿真》2024年第7期461-466,495,共7页Computer Simulation

基　　金：国家重点研发计划(2021YFE0104400);国家自然科学基金(62102187)。

摘　　要：跨社交网络身份匹配技术可以判断来自不同社交网络的账号是否属于同一个自然人。但社交网络拓扑结构复杂,数据庞大,因此匹配准确的同时节省计算资源与时间仍然是一个巨大的挑战。针对上述问题,提出基于社区结构与社交关系的跨社交网络身份匹配方法(IMCS)。IMCS利用社交结构与跨网络对齐的互动值向量进行用户表征,解决用户跨网络社交数据分布具有差异性的问题,并通过划分跨网络社区结构提出优先匹配机制,减少无效匹配次数并提高匹配的精度。基于真实社交网络数据集的实验结果表明,IMCS的表现优于对比算法,并在提升跨社交网络身份匹配准确率的同时降低无效匹配次数。User identity linkage across social networks can determine whether different accounts from diverse social networks belong to the same identity.However,the complex topology of social netwo rks and the huge amount of data make it a huge challenge to match accura tely while saving computational resources and time.To address these problems,a cross-social network identity matching method based on community structure and social relationships(IMCS)is proposed.IMCS uses social structures and cross-network aligned interaction vectors for user representation to tackle the challenge of disparities in the distribution of user data across social netw orks,and we propose a priority matching mechanism by dividing the cross-net work community structure to reduce the number of invalid matches and improve the accuracy of matching.Experimental evaluations are conducted based on real social network datasets demonstrate that IMCS outperforms the comparison methods and reduces the number of invalid matches while improving the cross-network identity matching accuracy.

关 键 词：社交网络 用户匹配 跨网络 网络嵌入 社区结构 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]