A Novel Greedy FluidSpread Algorithm With Equilibrium Temperature for Influence Diffusion in Social Networks

Maximizing positive influenced users (MPIU) is one of the most important and classic problems in social networks. In this article, we propose an effective solution to the problem of MPIU with positive top-k influence, namely, Greedy FluidSpread Algorithm with Equilibrium Temperature (GFAET). In this article, the behavior of the users, such as the interactions and relationships of each user, and the content of a topic aremodeled to the user interest vector and the topic distribution vector, respectively, to calculate the information acceptance probability. The influence diffusion process in social network is modeled as a fluid dynamics system and the attitude of the user is modeled as the fluid temperature. Newton's law of cooling and fluid dynamics theory is utilized to obtain amore accurate value of equilibrium temperature. Important users are then greedily selected in this system. Extensive experiments demonstrate that our GFAET significantly outperforms other traditional methods in terms of positive influence spread on both artificially generated and real-trace network datasets.