DRIMUX: Dynamic Rumor Influence Minimization with User Experience in Social Networks

Rumor blocking is a serious problem in large-scale social networks. Malicious rumors could cause chaos in society and hence need to be blocked as soon as possible after being detected. In this paper, we propose a model of dynamic rumor influence minimization with user experience (DRIMUX). Our goal is to minimize the influence of the rumor (i. e. , the number of users that have accepted and sent the rumor) by blocking a certain subset of nodes. A dynamic Ising propagation model considering both the global popularity and individual attraction of the rumor is presented based on realistic scenario. In addition, different from existing problems of in- fluence minimization, we take into account the constraint of user experience utility. Specifically, each node is assigned a tolerance time threshold. If the blocking time of each user exceeds that threshold, the utility of the network will decrease. Under this constraint, we then formulate the problem as a network inference problem with survival theory, and propose solutions based on maximum likelihood principle. Experiments are implemented based on large-scale real world networks and validate the effectiveness of our method.

Authors

• Biao Wang No affiliation
• Ge Chen Tiangong University
• Luoyi Fu Shanghai Jiao Tong University
• Li Song Shanghai Jiao Tong University
• Xinbing Wang Shanghai Jiao Tong University
• Xue Liu McGill University

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