DiffRank: A novel algorithm for information diffusion detection in social networks

被引：0

作者：

Zhou, Dong-Hao ^{[1
,2
]}

Han, Wen-Bao ^{[2
,3
]}

机构：

[1] College of Computer, National University of Defense Technology

[2] State Key Laboratory of Mathematical Engineering and Advanced Computing

[3] PLA Information Engineering University

来源：

Jisuanji Xuebao/Chinese Journal of Computers | 2014年 / 37卷 / 04期

关键词：

Diffusion detection; Information diffusion; Random walk model; Social computing; Social networks;

D O I：

10.3724/SP.J.1016.2014.00884

中图分类号：

学科分类号：

摘要：

Given a social network, information diffusion detection can be modeled as selecting a set of nodes as observations to detect the spreading of information or rumors as quickly as possible. It can be well applied to fields like opinion leader detection, rumor detection, and public security. Incorporating network structure, node attribute and history information cascades, we propose a random walk based algorithm DiffRank to sort nodes according to their diffusion ability, then choose the top-k nodes on the list as observations to detect information diffusion. Experiments on real dataset of Sina Weibo show that DiffRank outperforms other algorithms with respect to information cascades coverage ratio, detection time and reduction of infected population. Besides, DiffRank can be implemented easily in distributed or parallel computing environment, achieving good scalability.

引用

页码：884 / 893

页数：9

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