Influence Maximization in Evolving Multi-Campaign Environments

Online Social Networks (OSNs) are being extensively used in a variety of campaigns, with the objective to raise the awareness of the audience regarding a specific piece of information, e.g, product awareness, political positions, etc. An important characteristic is the correlations of different strength and types, i.e., either positive or negative, that appear among the plethora of information diffused in the network. Therefore, the breadth of the diffusion of a specific campaign is significantly affected by the spread of the correlated information. In addition to the correlations of the diffusing information, another important factor affecting the spread of a specific information, is the network connectivity. Social networks are constantly evolving, with links among users being continuously formed or discontinued and communication patterns being highly fluctuating. The aim of this work is to address the problem of Influence Maximization in Online Social Networks by bridging the gap of the existing literature regarding the plethora of correlated information simultaneously propagating in the network and the dynamic communication patterns observed among users in the network. Towards this, we propose a mechanism that estimates users' interaction probabilities and formulate a propagation model that captures how the exposure to many correlated campaigns impacts on the users' behavior to support any of them. We finally design a greedy seed selection approach that approximates the optimal solution at a ratio of 1 - 1/e and prove through extensive experimental evaluation the superiority of our approach compared to state-of-the-art approaches.