Читать книгу Intelligent Data Analytics for Terror Threat Prediction - Группа авторов - Страница 53

Many researchers believe that rumor diffusion occurs from only a single source in network, but in general people use several sources to boost the rumor diffusion quickly. Single source identification is comparatively easier and several approaches have already been developed, which are discussed in Section 1.6.1. In this section multiple source of rumor in network is discussed. Detection of multiple sources is proposed in Ref. [48]. They use BFS technique and give final tree after several observations. Following section explains some techniques to find multiple source of rumor. There are four methods: ranking-based, network partitioning, approximation-based and community-based.

In network partitioning rumor centrality is evaluated in two phases [20]. First phase, identify infected nodes and classify into groups, source in each group identified using rumor center method. Second phase, these recognized sources classified into two and in each again uses source estimator to identify source in that group.

Community-based is proposed in Ref. [21], by partitioning the community to identify several sources in every community. To recognize unseen and improved nodes use reverse diffusion approach through SIR model. Community detection method group all infected nodes into groups. Then apply single source detection approaches to find rumor source in each community. There are two more multiple source detection approaches such as ranking-based and approximation-based. For details see Ref. [10].