Spreading dynamics in complex networks
Spreading dynamics in complex networks
复制标题
在复杂网络中传播动态
DOI：
10.1088/1742-5468/2013/12/p12002
复制
发表时间：
2013-12-01
影响因子：
2.4
通讯作者：
Makse, Hernan A.
中科院分区：
文献类型：
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作者：
Pei, Sen;Makse, Hernan A.
在复杂网络中寻找有影响力的传播者对于各个领域的应用都具有重要意义，从流行病控制，创新扩散，病毒式营销和社会运动到思想传播。在本文中，我们首先展示了一些最重要的理论模型，描述了传播过程，然后分别讨论了定位个人和多个有影响力的传播者的问题。这两个主题的最新方法。针对特权单传播者的识别问题，本文总结了几种常用的中心度、介数中心度、PageRank、k-shell等方法，并对LiveJournal中的经验扩散数据进行了研究。有了这个广泛的数据集，我们发现各种措施可以传达非常不同的节点信息。在LiveJournal社交网络的所有用户中，只有一小部分参与了传播。对于LiveJournal中的传播过程，度可以以更高的概率定位参与信息扩散的节点，而k-shell更有效地找到影响力大的节点。我们的研究结果应该提供有用的信息，在现实中设计有效的传播策略。
Searching for influential spreaders in complex networks is an issue of great significance for applications across various domains, ranging from epidemic control, innovation diffusion, viral marketing, and social movement to idea propagation. In this paper, we first display some of the most important theoretical models that describe spreading processes, and then discuss the problem of locating both the individual and multiple influential spreaders respectively. Recent approaches in these two topics are presented. For the identification of privileged single spreaders, we summarize several widely used centralities, such as degree, betweenness centrality, PageRank, k-shell, etc. We investigate the empirical diffusion data in a large scale online social community-LiveJournal. With this extensive dataset, we find that various measures can convey very distinct information of nodes. Of all the users in the LiveJournal social network, only a small fraction of them are involved in spreading. For the spreading processes in LiveJournal, while degree can locate nodes participating in information diffusion with higher probability, k-shell is more effective in finding nodes with a large influence. Our results should provide useful information for designing efficient spreading strategies in reality.