Influence maximization through exploring structural information

作者全名："Li, Qi; Cheng, Le; Wang, Wei; Li, Xianghua; Li, Shudong; Zhu, Peican"

作者地址："[Li, Qi; Cheng, Le] Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China; [Cheng, Le; Li, Xianghua; Zhu, Peican] Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China; [Wang, Wei] Chongqing Med Univ, Sch Publ Hlth, Chongqing 400016, Peoples R China; [Li, Shudong] Guangzhou Univ, Cyberspace Inst Adv Technol, Guangzhou 510006, Peoples R China"

通信作者："Zhu, PC (通讯作者)，Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect iOPEN, Xian 710072, Peoples R China."

来源：APPLIED MATHEMATICS AND COMPUTATION

ESI学科分类：MATHEMATICS

WOS号：WOS:000903958900008

JCR分区：Q1

影响因子：3.5

年份：2023

卷号：442

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Influence maximization; Community detection; Seed set identification; Dynamic propagation

摘要："Influence maximization (IM) is a widely investigated issue in the study of social networks because of its potential commercial and social value. The purpose of IM is to identify a group of influential nodes that will spread information to other nodes in a network while simultaneously maximizing the number of nodes that are ultimately influenced. Traditional IM methods have different limitations, such as limited scalability to address large-scale networks and the neglect of community structural information. Here, we propose a novel influence maximization approach, i.e., the layered gravity bridge algorithm (LGB), to ad-dress the IM problem, which emphasizes the local structural information of networks and combines community detection algorithms with an improved gravity model. With the pro-posed LGB, a community detection method is used to derive the communities, and then the bridge nodes are found, which can be regarded as possible candidate seeds. Later, com-munities are merged into larger communities according to our proposed algorithm, and new bridge nodes are determined. Finally, all candidate seed nodes are sorted through an improved gravity model to determine the final seed nodes. The algorithm fully explores the network structural information provided by the communities, thereby making it su-perior to the current algorithms in terms of the number of ultimately infected nodes. Fur-thermore, our proposed algorithm possesses the potential to alleviate the influence overlap effect of seed nodes. To verify the effect of our approach, the classical SIR model is adopted to propagate information with the selected seed nodes, while experiments are performed on several practical datasets. As indicated by the obtained results, the performance of our proposed algorithm outperforms existing ones. (c) 2022 Elsevier Inc. All rights reserved."

基金机构："National Natural Science Foundation of China [U1803263, 62025602, 62073263, 62271411]; Key R&D Program of Guangzhou [202206030001]; Key Research and Development Program of Shaanxi Province [2022KW-26]; Natural Science Basic Research Plan in Shaanxi Province of China [2022JM325]; Fok Ying-Tong Education Foundation, China [171105]; Technological Innovation Team of Shaanxi Province [2020TD-013]"

基金资助正文："This work was supported in part by the National Natural Science Foundation of China (Grant Nos. U1803263, 62025602, 62073263, 62271411), Key R&D Program of Guangzhou (Grant No. 202206030001), Key Research and Development Program of Shaanxi Province (Grant No. 2022KW-26), Natural Science Basic Research Plan in Shaanxi Province of China (No. 2022JM325), the Fok Ying-Tong Education Foundation, China (Grant No. 171105), the Technological Innovation Team of Shaanxi Province (Grant No. 2020TD-013)."