An Effective Community-Based Greedy Approach Over Selected Communities for Influence Maximization.

Influence maximization refers to reaching the most promising part of a network by selecting a small number of nodes. Computing the maximum influence spread throughout a network based on influential seed sets is a computationally hard problem. This article attempts to minimize the execution time and maximize the influence spread. The technique is community based and comprises three steps, (1) Identify communities within a network, (2) Locate most significant communities and candidate vertices, and (3) Trace most influential nodes among candidate vertices. Experiments have been conducted on two real-world social network data sets to understand the efficacy and usefulness of the proposed algorithm.