Influence maximization

Total Influence and Hybrid Simulation of Independent Cascade Model using Rough Knowledge Granules

The paper defines a new theoretical measure Total Influence, of a node as well as a set of nodes in the social network. Total influence uses probabilistic theory to obtain the expected size of the information spreading in the social network under the …

Deprecation based greedy strategy for target set selection in large scale social networks

The problem of target set selection for large scale social networks is addressed in the paper. We describe a novel deprecation based greedy strategy to be applied over a pre-ordered (as obtained with any heuristic influence function) set of nodes. …

FGSN: Fuzzy Granular Social Networks - Model and applications

Social network data has been modeled with several approaches, including Sociogram and Sociomatrices, which are popular and comprehensive. Similar to these we have developed here a novel modeling technique based on granular computing theory and fuzzy …

Centrality measures, upper bound, and influence maximization in large scale directed social networks

The paper addresses the problem of finding top k influential nodes in large scale directed social networks. We propose two new centrality measures, Diffusion Degree for independent cascade model of information diffusion and Maximum Influence Degree. …