Identify Influential Social Network Spreaders
Identify Influential Social Network Spreaders
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DOI：
10.1109/icdmw.2014.31
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发表时间：
2014-12
期刊：
影响因子：
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通讯作者：
Chung-Yuan Huang;Yu-Hsiang Fu;Chuen-Tsai Sun
中科院分区：
文献类型：
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作者：
Chung-Yuan Huang;Yu-Hsiang Fu;Chuen-Tsai Sun
识别传播思想、信息或传染病的最有影响力的个人是网络研究人员高度关注的一个话题，因为这种识别可以帮助或阻碍信息传播、产品暴露或传染病检测。中心节点、高介数节点、高紧密度节点和高 k 壳节点已被确定为良好的初始传播者。然而，很少有人尝试使用网络结构内的节点多样性来衡量传播能力。本文描述的两步框架使用稳健且可靠的措施，结合全局多样性和局部特征来识别最有影响力的网络节点。一系列易感感染者恢复（SIR）流行病模拟的结果表明，我们提出的方法在与各种复杂网络数据集相关的单个初始传播者场景中表现良好且稳定。
Identifying the most influential individuals spreading ideas, information, or infectious diseases is a topic receiving significant attention from network researchers, since such identification can assist or hinder information dissemination, product exposure, or contagious disease detection. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, few efforts have been made to use node diversity within network structures to measure spreading ability. The two-step framework described in this paper uses a robust and reliable measure that combines global diversity and local features to identify the most influential network nodes. Results from a series of Susceptible-Infected-Recovered (SIR) epidemic simulations indicate that our proposed method performs well and stably in single initial spreader scenarios associated with various complex network datasets.