Capturing signatures of anomalous behavior in online social networks

This paper introduces PHYSENSE, a scalable framework for topic-dependent influence computation on large online social networks (OSNs) with application to generation of signatures of anomalous activity. PHYSENSE estimates and sets up sociological influence models to compute the diffusion of activity potential in the neighborhood of each of the nodes on the OSN. PHYSENSE then scales these to significant parts of the OSN by propagating the activity potentials through the graph topology, thereby generating the influence landscape in the form of an equivalent Green´s function matrix. The computationally efficient dynamic update phase of PHYSENSE tracks the time and topic dependent changes in the influence landscape.