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摘要
With their blistering expansion in recent years, popular online social sites such as Twitter, Facebook and Bebo, have become not only one of the most effective channels for viral marketing but also the major news sources for many people nowadays. Alongside these promising features, however, comes the threat of misinformation propagation which can lead to undesirable effects. Due to the sheer number of online social network (OSN) users and the highly clustered structures commonly shared by these kinds of networks, there is a substantial challenge to efficiently contain viral spread of misinformation in large-scale social networks. In this paper, we focus on how to limit viral propagation of misinformation in OSNs. Particularly, we study a set of problems, namely the beta(')(T)-Node Protectors problems, which aim to find the smallest set of highly influential nodes from which disseminating good information helps to contain the viral spread of misinformation, initiated from a set of nodes I, within a desired fraction (1 - beta) of the nodes in the entire network in T time steps. For this family of problems, we analyze and present solutions including their inaproximability results, greedy algorithms that provide better lower bounds on the number of selected nodes, and a community-based method for solving these problems. We further conduct a number of experiments on real-world traces, and the empirical results show that our proposed methods outperform existing alternative approaches in finding those important nodes that can help to contain the spread of misinformation effectively. Published by Elsevier B.V.
- 出版日期2013-7-5
