A heat diffusion model based algorithm for personalized influence maximization in social network

Article type: Research Article

Authors: Yang, Shuxin^{; *} | Wang, Xi | Liu, Chenghui

Affiliations: School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China. E-mail: [email protected]

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: Nowadays personalized influence maximization has become a new branch of influence maximization in social network. The existed methods mainly focus on independent cascade model and linear threshold model, in those two models, nodes’ influence forecast depends on the Monte-Carlo simulation. In order to simulate the propagation of influence more efficiently, we introduce heat diffusion model to the study of personalized influence maximization in this paper. The diffusion process of heat is adopted to simulate the influence of information among users. Furthermore, we propose a target’s heat greedy algorithm based on the analysis of the laws of the heat propagation. Experimental results on real datasets show that our proposed algorithm is effective.

Keywords: Social network, personalization, influence maximization, heat diffusion model

DOI: 10.3233/JHS-170561

Journal: Journal of High Speed Networks, vol. 23, no. 2, pp. 137-147, 2017