A node activity and connectivity-based model for influence maximization in social networks

Abstract

Influence maximization deals with the problem of identifying k-size subset of nodes in a social network that can maximize the influence spread in the network. In this paper, the problem of influence maximization using two aspects, node connectivity and node activity level, has been studied. To measure node connectivity, the widely popular and intuitive measure of out-degree of node has been used, and for node activity, node’s past interactions have been taken into consideration. For studying influence spread, two activity-based diffusion models, namely Activity-based Independent Cascade model and Activity-based Linear Threshold model, have been proposed in which influence propagation is driven by a node’s activity that it has actually performed in the past. Activity-based models aim at studying influence spread by incorporating a more realistic aspect corresponding to user behavior. Motivated by the belief that activity is as important as connectivity, UAC-Rank algorithm for the identification of initial adopters has been proposed.