Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction | |
Li, Dong1,2; Zhang, Shengping3; Sun, Xin3; Zhou, Huiyu4; Li, Sheng5; Li, Xuelong6; Li, D | |
作者部门 | 光学影像学习与分析中心 |
2017-09-01 | |
发表期刊 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING |
ISSN | 1041-4347 |
卷号 | 29期号:9页码:1985-1997 |
产权排序 | 6 |
摘要 | 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. |
文章类型 | Article |
关键词 | Information Diffusion Social Network Modeling Prediction |
学科领域 | Computer Science, Artificial Intelligence |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TKDE.2017.2702162 |
收录类别 | SCI |
关键词[WOS] | INFLUENCE MAXIMIZATION |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering |
项目资助者 | Hong Kong Scholar Foundation of China(ALGA4131016116) ; China Postdoctoral Foundation(2016M600250) ; Major Science and Technology Foundation of Shandong Province(2015ZDXX0201B02) ; Natural Science Foundation of China(61672188 ; Natural Science Foundation of Shandong Province(ZR2016FQ13) ; UK EPSRC(EP/N508664/1 ; Royal Society-Newton Advanced Fellowship(NA160342) ; National Natural Science Foundation of China(61761130079) ; 61602128) ; EP/N011074/1) |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000407433900016 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/29219 |
专题 | 光谱成像技术研究室 |
通讯作者 | Li, D |
作者单位 | 1.Shandong Univ, Sch Elect & Informat Engn, Weihai 264209, Peoples R China 2.Harbin Inst Technol, Harbin 150001, Heilongjiang, Peoples R China 3.Harbin Inst Technol, Sch Comp Sci & Technol, Weihai 264200, Peoples R China 4.Queens Univ Belfast, CSIT, Belfast BT7 1NN, Antrim, North Ireland 5.Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China 6.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China |
推荐引用方式 GB/T 7714 | Li, Dong,Zhang, Shengping,Sun, Xin,et al. Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2017,29(9):1985-1997. |
APA | Li, Dong.,Zhang, Shengping.,Sun, Xin.,Zhou, Huiyu.,Li, Sheng.,...&Li, D.(2017).Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,29(9),1985-1997. |
MLA | Li, Dong,et al."Modeling Information Diffusion over Social Networks for Temporal Dynamic Prediction".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 29.9(2017):1985-1997. |
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