Influence maximization in social networks

The main challenge in viral marketing, that is powered by social networks, is to minimize the seed set that will initiate the diffusion process and maximize the total influence at its termination. The aim of this thesis is to study influence propagation models under the influence maximization problem and to investigate the effectiveness of a new model that is based on a multi-objective approach. We propose a Depth-Based Diminishing Influence model (DBDM) that is based on adding nodes to the seed set by considering influenced in-neighbors and how far these in-neighbors are from the initial activated set. As an enhancement to our approach, we used a clustering mechanism to help increase the influence spread. Several experiments were conducted to compare between our approach and previous work. As a result, the selection of the seed set under the DBDM model boosted the influence spread substantially compared to previously proposed models.