Link prediction-based influence maximization in online social networks

Abstract

Influence Maximization (IM) is the problem of finding a small set of highly influential users in the social networks. The influence spreads according to an explicit influence propagation model. IM is an essential component in many applications such as Network Monitoring and Viral Marketing. Most of the present IM solutions neglect the highly dynamic behavior of social networks. It can result in either deprived seed qualities or a prolonged processing time. In this paper, we study the IM problem in a social network that evolves with time and proposes a new Link Prediction based Influential Node Tracking (LPINT) framework. In the proposed model, we apply the conditional temporal Restricted Boltzmann Machine (ctRBM) to predict the upcoming snapshot of the graph by predicting the links that may appear in the network by considering the evolutionary network's temporal and structural pattern. And then, we apply an efficient IM technique for finding the seed nodes in the predicted snapshot of the network. Finally, we evaluate the spread of influence in the latest snapshot of the graph using predicted seed nodes. Extensive experimentation on four real large-scale datasets confirms that our LPINT model attains better performance in terms of influence coverage and influence spread time for considered networks compared to the baseline techniques. © 2021 Elsevier B.V.