Abstract: I explore the problem of rumour source identification in time-varying social networks that can be reduced to a series of static networks by introducing a time-integrating window. I borrow an idea from criminology and propose a novel method to overcome the challenges. First, I reduce the time-varying networks to a series of static networks by introducing a time-integrating window. Second, instead of inspecting every individual in traditional techniques, I adopt a reverse dissemination strategy to specify a set of suspects of the real rumour source. This process addresses the scalability issue of source identification problems, and therefore dramatically promotes the efficiency of rumour source identification. Third is to determine the real source from the suspects. Information that propagates through social networks can carry a lot of false claims. For example, rumours on certain topics can propagate rapidly leading to a large number of nodes reporting the same (incorrect) observations. In this paper, I describe an approach for finding the rumour source and assessing the likelihood that a piece of information is in fact a rumour, in the absence of data provenance information. I model the social network as a directed graph, where vertices represent individuals and directed edges represent information flow. A number of monitor nodes are injected into the network whose job is to report data they receive.
Keywords: Source identification, Time Varying, Static Networks, Social Networks.