{"title":"Identifying influential nodes in social networks from the perspective of attack-defense game.","authors":"Wen Hu, Ye Deng, Yu Xiao, Jun Wu","doi":"10.1063/5.0240052","DOIUrl":null,"url":null,"abstract":"<p><p>Influence spread analysis, a critical component of social network studies, focuses on the patterns and effects of information dissemination among interconnected entities. The core of influence spread analysis is to identify influential nodes that involve two distinct aspects: influence maximization (IM) and influence blocking maximization (IBM). However, when IM and IBM occur simultaneously, identifying influential nodes becomes an intricate decision-making challenge. This study addresses identifying influential nodes in social networks through an attack-defense game perspective, where an attacker maximizes influence and a defender minimizes it. We first develop a two-player static zero-sum game model considering resource constraints. Based on the equilibrium strategy of this game, we redefine the concept of influential nodes from various viewpoints. Extensive experiments on synthetic and real-world networks show that, in most cases, the defender preferentially defends critical nodes, while the attacker adopts the decentralized strategy. Only when resources are unevenly matched do both players tend to adopt centralized strategies. This study expands the connotation of influential nodes and provides a novel paradigm for the social network analysis with significant potential applications.</p>","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"34 11","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chaos","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1063/5.0240052","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
Influence spread analysis, a critical component of social network studies, focuses on the patterns and effects of information dissemination among interconnected entities. The core of influence spread analysis is to identify influential nodes that involve two distinct aspects: influence maximization (IM) and influence blocking maximization (IBM). However, when IM and IBM occur simultaneously, identifying influential nodes becomes an intricate decision-making challenge. This study addresses identifying influential nodes in social networks through an attack-defense game perspective, where an attacker maximizes influence and a defender minimizes it. We first develop a two-player static zero-sum game model considering resource constraints. Based on the equilibrium strategy of this game, we redefine the concept of influential nodes from various viewpoints. Extensive experiments on synthetic and real-world networks show that, in most cases, the defender preferentially defends critical nodes, while the attacker adopts the decentralized strategy. Only when resources are unevenly matched do both players tend to adopt centralized strategies. This study expands the connotation of influential nodes and provides a novel paradigm for the social network analysis with significant potential applications.
影响力传播分析是社会网络研究的重要组成部分,主要研究相互关联的实体之间的信息传播模式和效果。影响力传播分析的核心是识别有影响力的节点,其中涉及两个不同的方面:影响力最大化(IM)和影响力阻断最大化(IBM)。然而,当 IM 和 IBM 同时发生时,识别有影响力的节点就成了一个复杂的决策难题。本研究通过攻击-防御博弈的视角来识别社交网络中的有影响力节点,即攻击者最大化影响力,防御者最小化影响力。我们首先建立了一个考虑到资源限制的双人静态零和博弈模型。基于该博弈的均衡策略,我们从不同角度重新定义了有影响力节点的概念。在合成网络和真实世界网络上进行的大量实验表明,在大多数情况下,防御方优先防御关键节点,而攻击方则采取分散策略。只有当资源匹配不均衡时,双方才会倾向于采用集中式策略。这项研究拓展了有影响力节点的内涵,为社会网络分析提供了一种新的范式,具有重要的潜在应用价值。
期刊介绍:
Chaos: An Interdisciplinary Journal of Nonlinear Science is a peer-reviewed journal devoted to increasing the understanding of nonlinear phenomena and describing the manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines.