Maximizing Influence in Temporal Social Networks:A Node Feature-Aware Voting Algorithm  

作　　者：Wenlong Zhu Yu Miao Shuangshuang Yang Zuozheng Lian Lianhe Cui 

机构地区：[1]College of Computer and Control Engineering,Qiqihar University,Qiqihar,161006,China [2]Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis,Qiqihar University,Qiqihar,161006,China [3]College of Teacher Education,Qiqihar University,Qiqihar,161006,China

出　　处：《Computers, Materials & Continua》2023年第12期3095-3117,共23页计算机、材料和连续体（英文）

基　　金：supported by the Fundamental Research Funds for the Universities of Heilongjiang(Nos.145109217,135509234);the Youth Science and Technology Innovation Personnel Training Project of Heilongjiang(No.UNPYSCT-2020072);the Innovative Research Projects for Postgraduates of Qiqihar University(No.YJSCX2022048).

摘　　要：Influence Maximization(IM)aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes.However,most existing studies on the IM problem focus on static social network features,while neglecting the features of temporal social networks.To bridge this gap,we focus on node features reflected by their historical interaction behavior in temporal social networks,i.e.,interaction attributes and self-similarity,and incorporate them into the influence maximization algorithm and information propagation model.Firstly,we propose a node feature-aware voting algorithm,called ISVoteRank,for seed nodes selection.Specifically,before voting,the algorithm sets the initial voting ability of nodes in a personalized manner by combining their features.During the voting process,voting weights are set based on the interaction strength between nodes,allowing nodes to vote at different extents and subsequently weakening their voting ability accordingly.The process concludes by selecting the top k nodes with the highest voting scores as seeds,avoiding the inefficiency of iterative seed selection in traditional voting-based algorithms.Secondly,we extend the Independent Cascade(IC)model and propose the Dynamic Independent Cascade(DIC)model,which aims to capture the dynamic features in the information propagation process by combining node features.Finally,experiments demonstrate that the ISVoteRank algorithm has been improved in both effectiveness and efficiency compared to baseline methods,and the influence spread through the DIC model is improved compared to the IC model.

关 键 词：Temporal social networks influence maximization voting strategy interactive properties SELF-SIMILARITY 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]