Abstract
Rumor that propagates through online social networks can carry a lot of negative effects and even disturb the social order. This paper addresses the problem of detecting the rumor source in an online social network based on an observed snapshot. We assume the spreading of a rumor in the social networks follows the susceptible-exposed-infected-recovered (SEIR) model. All nodes are assumed initially in susceptible states, but only one single rumor source is in infected state. The susceptible node receives messages from its infected neighbor social nodes and it can be treated as exposed at each time-slot. Once an exposed node believes these received messages and forwarded them, it would turn into the infected state; otherwise, it would drop these messages and then it is considered as in the recovered state. It is assumed that the recovered nodes will never believe these information again. Given an observed snapshot of online social network, in which the susceptible nodes, exposed nodes and recovered nodes cannot be distinguished, the estimator is evaluated to identify the source associated with the most likely infection process based on induction hypotheses. The effectiveness of the proposed method is validated using experiments based on a tree networks and two real-world networks, and the results demonstrate that our estimator performs better than the existing closeness centrality heuristic.
Our work was jointly supported by the National Natural Science Foundation of China (No. 61702067, No. 61672119, No. 6147264), the Chongqing Research Program of Application Foundation and Advanced Technology (No. cstc2017jcyjAX0201), the Science and Technology Research Project of Chongqing Municipal Education Commission (No. KJ1600445).
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Zhou, Y., Wu, C. (2018). Identifying Rumor Source of Online Social Networks in the SEIR Model. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11067. Springer, Cham. https://doi.org/10.1007/978-3-030-00018-9_34
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