Influence Maximization: Incorporating Homophily and Topological Overlap

Abstract

Research on Influence Maximization has gained a lot of attention in the recent past. Part of the reason for this is that influence maximization has applications in commercially attractive areas such as word of mouth marketing. A majority of works in influence maximization have relied on information diffusion models that largely ignore the structural properties of the social network. The problem with this is that important attributes of user relationships which are necessary for approximating influence maximization are ignored. Parameters such as Homophily and Topological Overlap are crucial determinants of the level of influence that a user enjoys in the network. This work approximates global influence power of a user by first, considering user interactions, homophily and topological overlap as determinants of node to node relationship strength on a dynamic social graph. Then secondly, computes a global score of influence for each user. We then apply a novel algorithm that approximates influence spread for each influential user. The seed set is built by identifying the most influential users at specific time instances as the social graph evolves.