Time-sensitive Positive Influence Maximization in signed social networks

Abstract Influence maximization (IM) in social networks has attracted a wide variety of studies motivated by applications like spread of ideas or innovations in a network and viral marketing of products. How to integrate the factors affecting influence diffusion into influence maximization is an key research direction. Timeliness of influence diffusion and the polarity of influence itself are two important factors, which have been used to extend IM problem by researchers. However, all existing studies only focused on one of two factors individually, this will lead time delay of influence propagation or inaccurate influence estimation. Aiming at this drawback, we propose the Time-sensitive Positive Influence Maximization (TP-IM) problem considering two factors simultaneously, to select the seed node set achieving maximum positive influence spread within the specified time limit. Furthermore, we construct the Heat Diffusion-based Polarity Influence Diffusion (HDPID) model and an improved k -Step Greedy seed node selection algorithm, to solve the TP-IM problem. Experimental results on three signed social network datasets, Epinions, Slashdot, and Wikipedia demonstrate that our method outperforms state-of-the-art methods in terms of positive influence spread with time limit.