posted on 2018-02-16, 15:02authored byDong Li, Shengping Zhang, Xin Sun, Huiyu Zhou, Sheng Li, Xuelong Li
Modeling the process of information diffusion is a challenging problem. Although numerous attempts have been made in order to solve this problem, very few studies are actually able to simulate and predict temporal dynamics of the diffusion process. In this paper, we propose a novel information diffusion model, namely GT model, which treats the nodes of a network as intelligent and rational agents and then calculates their corresponding payoffs, given different choices to make strategic decisions. By introducing time-related payoffs based on the diffusion data, the proposed GT model can be used to predict whether or not the user's behaviors will occur in a specific time interval. The user's payoff can be divided into two parts: social payoff from the user's social contacts and preference payoff from the user's idiosyncratic preference. We here exploit the global influence of the user and the social influence between any two users to accurately calculate the social payoff. In addition, we develop a new method of presenting social influence that can fully capture the temporal dynamics of social influence. Experimental results from two different datasets, Sina Weibo and Flickr demonstrate the rationality and effectiveness of the proposed prediction method with different evaluation metrics.
Funding
D. Li was supported in part by the Hong Kong Scholar
Foundation of China (No. ALGA4131016116), the China
Postdoctoral Foundation (No. 2016M600250), and the Major
Science and Technology Foundation of Shandong Province
(No. 2015ZDXX0201B02). S. Zhang was supported in part
by the Natural Science Foundation of China (No. 61672188).
X. Sun was supported in part by the Natural Science Foundation
of China (No. 61602128) and the Natural Science
Foundation of Shandong Province (No. ZR2016FQ13).
H. Zhou was supported in part by UK EPSRC under Grants
EP/N508664/1 and EP/N011074/1, and Royal SocietyNewton
Advanced Fellowship under Grant NA160342.
X. Li was supported in part by the National Natural Science
Foundation of China (Grant No. 61761130079).
History
Citation
IEEE Transactions on Knowledge and Data Engineering, 2017, 29 (9), pp. 1985-1997 (13)
Author affiliation
/Organisation
Version
AM (Accepted Manuscript)
Published in
IEEE Transactions on Knowledge and Data Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)