An efficient algorithm for mining a set of influential spreaders in complex networks
An efficient algorithm for mining a set of influential spreaders in complex networks
复制标题
一种在复杂网络中挖掘一组有影响力的传播者的有效算法
DOI：
10.1016/j.physa.2018.10.011
复制
发表时间：
2019-02-15
影响因子：
3.3
通讯作者：
Ruan, Yirun
中科院分区：
文献类型：
--
作者：
Jiang, Lincheng;Zhao, Xiang;Ruan, Yirun
识别社会网络中的影响节点对于信息传播、病毒控制和传染病检测具有重要意义。本文将有影响力传播者的选择问题转化为一个寻找稠密连接群的问题。受k壳分解法去除外围节点后网络聚类系数增加的启发，选取k壳值最高且相互连通的节点作为核心，形成初始组。然后将与该组紧密相连的邻居节点逐渐加入到该组中，最后选择每个稠密组中度中心性最强的节点作为初始传播者，并将该组中所有节点的k-shell值设置为0，然后搜索下一个组。因此，该方法不仅可以保证传播者本身是有影响力的，而且它们之间的距离是相对分散的。在6个真实的网络上的实验结果表明，该方法识别出的传播者比折扣度方法、VoteRank、LIR、k-shell和度中心性等基准算法有更好的影响力。(C)2018爱思唯尔B. V.保留所有权利。
Identifying the influential nodes in social network is of significance for information spreading, virus control and contagious disease detection. In this paper, the problem of influential spreaders selection is transferred into a problem to find groups with dense connections. Inspired by the fact that the network clustering coefficient would increase with the removal of peripheral nodes by the k-shell decomposition method, we select nodes with the highest k-shell value and interconnected with each other as the core to form an initial group. Then the neighbour nodes closely connected to the group are gradually added into it. The most influential node identified by degree centrality in each dense group would finally be selected as the initial spreaders and the k-shell value of all nodes in the group are set to 0 before searching for the next group. Therefore, the proposed method can guarantee not only the spreaders themselves are influential, but also the distance among them is relatively scattered. The experimental results in six real networks indicate that the spreaders identified by the method are more influential than several benchmark algorithms, including the discount degree method, VoteRank, LIR, k-shell and degree centrality. (C) 2018 Elsevier B.V. All rights reserved.