Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/41194
Title: Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction
Authors: Li, Dong
Zhang, Shengping
Sun, Xin
Zhou, Huiyu
Li, Sheng
Li, Xuelong
First Published: 8-May-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: IEEE Transactions on Knowledge and Data Engineering, 2017, 29 (9), pp. 1985-1997 (13)
Abstract: 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.
DOI Link: 10.1109/TKDE.2017.2702162
ISSN: 1041-4347
eISSN: 1558-2191
Links: http://ieeexplore.ieee.org/document/7921565/
http://hdl.handle.net/2381/41194
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2017, IEEE. Deposited with reference to the publisher’s open access archiving policy.
Appears in Collections:Published Articles, Dept. of Computer Science

Files in This Item:
File Description SizeFormat 
final.2.pdfPost-review (final submitted author manuscript)558.26 kBAdobe PDFView/Open


Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.