{"title":"The effect of network topology on the spread of epidemics","authors":"A. Ganesh, L. Massoulié, D. Towsley","doi":"10.1109/INFCOM.2005.1498374","DOIUrl":null,"url":null,"abstract":"Many network phenomena are well modeled as spreads of epidemics through a network. Prominent examples include the spread of worms and email viruses, and, more generally, faults. Many types of information dissemination can also be modeled as spreads of epidemics. In this paper we address the question of what makes an epidemic either weak or potent. More precisely, we identify topological properties of the graph that determine the persistence of epidemics. In particular, we show that if the ratio of cure to infection rates is larger than the spectral radius of the graph, then the mean epidemic lifetime is of order log n, where n is the number of nodes. Conversely, if this ratio is smaller than a generalization of the isoperimetric constant of the graph, then the mean epidemic lifetime is of order e/sup na/, for a positive constant a. We apply these results to several network topologies including the hypercube, which is a representative connectivity graph for a distributed hash table, the complete graph, which is an important connectivity graph for BGP, and the power law graph, of which the AS-level Internet graph is a prime example. We also study the star topology and the Erdos-Renyi graph as their epidemic spreading behaviors determine the spreading behavior of power law graphs.","PeriodicalId":20482,"journal":{"name":"Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies.","volume":"6 1","pages":"1455-1466 vol. 2"},"PeriodicalIF":0.0000,"publicationDate":"2005-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"805","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INFCOM.2005.1498374","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 805
Abstract
Many network phenomena are well modeled as spreads of epidemics through a network. Prominent examples include the spread of worms and email viruses, and, more generally, faults. Many types of information dissemination can also be modeled as spreads of epidemics. In this paper we address the question of what makes an epidemic either weak or potent. More precisely, we identify topological properties of the graph that determine the persistence of epidemics. In particular, we show that if the ratio of cure to infection rates is larger than the spectral radius of the graph, then the mean epidemic lifetime is of order log n, where n is the number of nodes. Conversely, if this ratio is smaller than a generalization of the isoperimetric constant of the graph, then the mean epidemic lifetime is of order e/sup na/, for a positive constant a. We apply these results to several network topologies including the hypercube, which is a representative connectivity graph for a distributed hash table, the complete graph, which is an important connectivity graph for BGP, and the power law graph, of which the AS-level Internet graph is a prime example. We also study the star topology and the Erdos-Renyi graph as their epidemic spreading behaviors determine the spreading behavior of power law graphs.
许多网络现象可以很好地模拟为流行病通过网络的传播。突出的例子包括蠕虫和电子邮件病毒的传播,以及更普遍的故障。许多类型的信息传播也可以模拟为流行病的传播。在本文中,我们讨论是什么使流行病变弱或变强的问题。更准确地说,我们确定了决定流行病持久性的图的拓扑性质。特别地,我们证明了如果治愈率与感染率之比大于图的谱半径,则平均流行病寿命为log n阶,其中n为节点数。相反,如果这个比率小于泛化等周常数的图,顺序的流行一生是e / na /一同晚餐,积极的常数。我们将这些结果应用到几种网络拓扑包括超立方体,这是一个代表分布式哈希表连接图,完全图,这是一个重要的连接图边界网关协议,和幂律图,等级的网络图是一个典型的例子。我们还研究了星型拓扑和Erdos-Renyi图,因为它们的流行传播行为决定了幂律图的传播行为。