Identifying influential nodes in social networks via community structure and influence distribution difference  

作　　者：Zufan Zhang Xieliang Li Chenquan Gan 

机构地区：[1]School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing,400065,China [2]Chongqing Key Laboratory of Mobile Communications Technology,Chongqing,400065,China [3]Engineering Research Center of Mobile Communications,Ministry of Education,Chongqing,400065,China

出　　处：《Digital Communications and Networks》2021年第1期131-139,共9页数字通信与网络（英文版）

基　　金：The authors are grateful to the anonymous reviewers and the editor for their valuable comments and suggestions.This work is supported by Natural Science Foundation of China(Grant Nos.61702066 and 11747125);Major Project of Science and Technology Research Program of Chongqing Education Commission of China(Grant No.KJZD-M201900601);Chongqing Research Program of Basic Research and Frontier Technology(Grant Nos.cstc2017jcyjAX0256 and cstc2018jcy-jAX0154);Project Supported by Chongqing Municipal Key Laboratory of Institutions of Higher Education(Grant No.cqupt-mct-201901);Tech-nology Foundation of Guizhou Province(QianKeHeJiChu[2020]1Y269);New academic seedling cultivation and exploration innovation project(QianKeHe Platform Talents[2017]5789-21).

摘　　要：This paper aims to effectively solve the problem of the influence maximization in social networks.For this purpose,an influence maximization method that can identify influential nodes via the community structure and the influence distribution difference is proposed.Firstly,the network embedding-based community detection approach is developed,by which the social network is divided into several high-quality communities.Secondly,the solution of influence maximization is composed of the candidate stage and the greedy stage.The candidate stage is to select candidate nodes from the interior and the boundary of each community using a heuristic algorithm,and the greedy stage is to determine seed nodes with the largest marginal influence increment from the candidate set through the sub-modular property-based Greedy algorithm.Finally,experimental results demonstrate the superiority of the proposed method compared with existing methods,from which one can further find that our work can achieve a good tradeoff between the influence spread and the running time.

关 键 词：Social network Community detection Influence maximization Network embedding Influence distribution difference 

分 类 号：TN91[电子电信—通信与信息系统]