Fair-Aware Competitive Event Influence Maximization in Social Networks

Abstract

With the emergence and rapid development of event-based social networks (EBSNs), the problem of how to maximize the number of participants for social events has been widely studied. Most previous studies on this problem focus on a single social event. However, in real-world scenarios, there are multiple social events and they may compete for influential users to maximize the number of participants since they may be held during the same time period and in acceptable geographic locations. To bridge this gap, in this paper, we propose to study a novel problem of Fair-Aware Competitive Event Influence Maximization (FCE-IM) . This problem is how to select influential users for each social event on the premise of fairness to maximize the number of participants per event in the competitive scenario. To solve this problem, we first propose a propagation model, named E-LT , to describe the information propagation process in competitive situation in EBSNs. Then, we propose a Randomized Algorithm based on Cross Entropy method , named RACE . It can optimally assign node selection probability for each node and thus effectively approach to the maxima. We conduct extensive experiments using two real-world datasets and experimental results demonstrate the efficiency and effectiveness of our algorithm.