PHYSENSE: Scalable sociological interaction models for influence estimation on online social networks

The explosion in social media adoption has opened up new opportunities for next-generation personalized web and information exchange in big data scenarios. Making sense of the massive number of overlapping streams of information generated by hundreds of millions of users on large social networks requires novel analytics and scalable computational techniques. This paper introduces PHYSENSE, a scalable framework for influence computation and activity prediction on large online social networks (OSNs). Drawing inspiration from the Friedkin-Johnsen model for opinion change, PHYSENSE estimates and sets up sociological influence models to compute the diffusion of activity potential in the neighborhood of each of the nodes. PHYSENSE then scales these to significant parts of the entire OSN by propagating these activity potentials through an equivalent Helmholtz Green's function. Examples to show the enhanced quality of PHYSENSE in influence detection over popular existing methods based on variations of PageRank are presented. Additionally, for enhanced speedup, the community structures found in the social graphs along with low-rank updates are exploited in the acceleration of both the setup and the dynamic update phases of the influence computation.