Influence Maximization Based on Snapshot Prediction in Dynamic Online Social Networks

Abstract

With the vigorous development of the mobile Internet, online social networks have greatly changed the way of life of human beings. As an important branch of online social network research, influence maximization refers to finding K nodes in the network to form the most influential seed set, which is an abstract model of viral marketing. Most of the current research is based on static network structures, ignoring the important feature of network structures changing with time, which discounts the effect of seed nodes in dynamic online social networks. To address this problem in dynamic online social networks, we propose a novel framework called Influence Maximization based on Prediction and Replacement (IMPR). This framework first uses historical network snapshot information to predict the upcoming network snapshot and then mines seed nodes suitable for the dynamic network based on the predicted result. To improve the computational efficiency, the framework also adopts a fast replacement algorithm to solve the seed nodes between different snapshots. The scheme we adopted exhibits four advantages. First, we extended the classic influence maximization problem to dynamic online social networks and give a formal definition of the problem. Second, a new framework was proposed for this problem and a proof of the solution is given in theory. Third, other classical algorithms for influence maximization can be embedded into our framework to improve accuracy. More importantly, to reveal the performance of the scheme, a series of experiments based on different settings on real dynamic online social network datasets were carried out, and the experimental results are very promising.