利用全球多样性和地方特征识别有影响力的社交网络传播者

Yu-Hsiang Fu, Chung-Yuan Huang, Chuen-Tsai Sun
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引用次数: 6

摘要

通过社会网络确定有影响力的信息传播者,可有助于加速或阻碍信息传播、增加产品曝光和发现传染病的爆发。Hub节点、高中间节点、高亲近节点和高k壳节点被认为是良好的初始传播者。然而,研究人员忽视了网络结构内节点多样性作为衡量网络传播能力的一种手段。本文描述的两步框架使用了一种鲁棒性和不敏感的度量,该度量结合了全球多样性和局部特征(例如,度中心性)来识别最具影响力的社会网络节点。初步实验结果表明,该方法在不同社交网络数据集的单一初始扩展场景下具有良好的性能和稳定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using global diversity and local features to identify influential social network spreaders
The identification of influential spreaders of information via social networks can assist in the acceleration or hindrance of information dissemination, in increased product exposure, and in the detection of contagious disease outbreaks. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, researchers have overlooked node diversity within network structures as a means of measuring spreading ability. The two-step framework described in this paper uses a robust and insensitive measure that combines global diversity and local features (e.g., degree centrality) to identify the most influential social network nodes. Preliminary experiment results indicate that the proposed method performs well and maintains stability in single initial spreader scenarios associated with different social network datasets.
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