Leiyang Chen , Ying Xi , Liang Dong , Manjun Zhao , Chenliang Li , Xiao Liu , Xiaohui Cui
{"title":"Identifying influential nodes in complex networks via Transformer","authors":"Leiyang Chen , Ying Xi , Liang Dong , Manjun Zhao , Chenliang Li , Xiao Liu , Xiaohui Cui","doi":"10.1016/j.ipm.2024.103775","DOIUrl":null,"url":null,"abstract":"<div><p>In the domain of complex networks, the identification of influential nodes plays a crucial role in ensuring network stability and facilitating efficient information dissemination. Although the study of influential nodes has been applied in many fields such as suppression of rumor spreading, regulation of group behavior, and prediction of mass events evolution, current deep learning-based algorithms have limited input features and are incapable of aggregating neighbor information of nodes, thus failing to adapt to complex networks. We propose an influential node identification method in complex networks based on the Transformer. In this method, the input sequence of a node includes information about the node itself and its neighbors, enabling the model to effectively aggregate node information to identify its influence. Experiments were conducted on 9 synthetic networks and 12 real networks. Using the SIR model and a benchmark method to verify the effectiveness of our approach. The experimental results show that this method can more effectively identify influential nodes in complex networks. In particular, the method improves 27 percent compared to the second place method in network Netscience and 21 percent in network Faa.</p></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4000,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing & Management","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306457324001353","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
In the domain of complex networks, the identification of influential nodes plays a crucial role in ensuring network stability and facilitating efficient information dissemination. Although the study of influential nodes has been applied in many fields such as suppression of rumor spreading, regulation of group behavior, and prediction of mass events evolution, current deep learning-based algorithms have limited input features and are incapable of aggregating neighbor information of nodes, thus failing to adapt to complex networks. We propose an influential node identification method in complex networks based on the Transformer. In this method, the input sequence of a node includes information about the node itself and its neighbors, enabling the model to effectively aggregate node information to identify its influence. Experiments were conducted on 9 synthetic networks and 12 real networks. Using the SIR model and a benchmark method to verify the effectiveness of our approach. The experimental results show that this method can more effectively identify influential nodes in complex networks. In particular, the method improves 27 percent compared to the second place method in network Netscience and 21 percent in network Faa.
期刊介绍:
Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing.
We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.