Rumor Source Identification in Social Networks with Time-Varying Topology - 2018


Identifying rumor sources in social networks plays a critical role in limiting the injury caused by them through the timely quarantine of the sources. But, the temporal variation in the topology of social networks and the continued dynamic processes challenge our ancient source identification techniques that are considered in static networks. During this Project, we tend to borrow an plan from criminology and propose a novel technique to overcome the challenges. First, we tend to cut back the time-varying networks to a series of static networks by introducing a time-integrating window. Second, instead of inspecting each individual in ancient techniques, we have a tendency to adopt a reverse dissemination strategy to specify a group of suspects of the important rumor source. This method addresses the scalability issue of source identification issues, and thus dramatically promotes the efficiency of rumor source identification. Third, to see the important source from the suspects, we employ a novel microscopic rumor spreading model to calculate the maximum likelihood (ML) for each suspect. The one who can offer the largest ML estimate is considered as the real source. The evaluations are distributed on real social networks with time-varying topology. The experiment results show that our methodology will cut back sixty - ninety p.c of the source seeking area in varied time-varying social networks. The results further indicate that our method will accurately determine the important source, or an individual who is very shut to the $64000 supply. To the best of our knowledge, the proposed technique is the first which will be used to identify rumor sources in time-varying social networks.

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