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类核状群组导致通过k-壳层分解识别超级传播者的方法失效。

Core-like groups result in invalidation of identifying super-spreader by k-shell decomposition.

作者信息

Liu Ying, Tang Ming, Zhou Tao

机构信息

1] Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China [2] School of Computer Science, Southwest Petroleum University, Chengdu 610500, China.

Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.

出版信息

Sci Rep. 2015 May 6;5:9602. doi: 10.1038/srep09602.

Abstract

Identifying the most influential spreaders is an important issue in understanding and controlling spreading processes on complex networks. Recent studies showed that nodes located in the core of a network as identified by the k-shell decomposition are the most influential spreaders. However, through a great deal of numerical simulations, we observe that not in all real networks do nodes in high shells are very influential: in some networks the core nodes are the most influential which we call true core, while in others nodes in high shells, even the innermost core, are not good spreaders which we call core-like group. By analyzing the k-core structure of the networks, we find that the true core of a network links diversely to the shells of the network, while the core-like group links very locally within the group. For nodes in the core-like group, the k-shell index cannot reflect their location importance in the network. We further introduce a measure based on the link diversity of shells to effectively distinguish the true core and core-like group, and identify core-like groups throughout the networks. Our findings help to better understand the structural features of real networks and influential nodes.

摘要

识别最具影响力的传播者是理解和控制复杂网络上传播过程的一个重要问题。最近的研究表明,通过k-壳分解确定位于网络核心的节点是最具影响力的传播者。然而,通过大量的数值模拟,我们观察到并非在所有真实网络中,高壳层的节点都非常有影响力:在一些网络中,核心节点是最具影响力的,我们称之为真正的核心,而在其他网络中,高壳层的节点,甚至是最内层的核心节点,都不是好的传播者,我们称之为类核心组。通过分析网络的k-核结构,我们发现网络的真正核心与网络的壳层有多样的连接,而类核心组在组内的连接非常局部。对于类核心组中的节点,k-壳指数不能反映它们在网络中的位置重要性。我们进一步引入一种基于壳层连接多样性的度量方法,以有效区分真正的核心和类核心组,并识别整个网络中的类核心组。我们的发现有助于更好地理解真实网络的结构特征和有影响力的节点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/897e/5386204/abc2fb38e892/srep09602-f1.jpg

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