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Understanding information interactions in diffusion: an evolutionary game-theoretic perspective

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Abstract

Social networks are fundamental mediums for diffusion of information and contagions appear at some node of the network and get propagated over the edges. Prior researches mainly focus on each contagion spreading independently, regardless of multiple contagions’ interactions as they propagate at the same time. In the real world, simultaneous news and events usually have to compete for user’s attention to get propagated. In some other cases, they can cooperate with each other and achieve more influences.

In this paper, an evolutionary game theoretic framework is proposed to model the interactions among multiple contagions. The basic idea is that different contagions in social networks are similar to the multiple organisms in a population, and the diffusion process is as organisms interact and then evolve from one state to another. This framework statistically learns the payoffs as contagions interacting with each other and builds the payoff matrix. Since learning payoffs for all pairs of contagions IS almost impossible (quadratic in the number of contagions), a contagion clustering method is proposed in order to decrease the number of parameters to fit, which makes our approach efficient and scalable. To verify the proposed framework, we conduct experiments by using real-world information spreading dataset of Digg. Experimental results show that the proposed game theoretic framework helps to comprehend the information diffusion process better and can predict users’ forwarding behaviors with more accuracy than the previous studies. The analyses of evolution dynamics of contagions and evolutionarily stable strategy reveal whether a contagion can be promoted or suppressed by others in the diffusion process.

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Correspondence to Xi Zhang.

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Yuan Su received his Master degree in computer science and technology from Beijing University of Posts and Telecommunications (BUPT), China in 2012. He is currently a PhD candidate there. His research interest is social network analysis and data mining. He is currently working on modeling and predicting the information diffusion in social networks.

Xi Zhang received his BS degree from Harbin Institute of Technology, China in 2006, and his PhD degree from Tsinghua University, China in 2012. He is now an assistant professor in School of Computer Science at Beijing University of Posts and Telecommunications (BUPT), China. He is also in Key Laboratory of Trustworthy Distributed Computing and Service ofMinistry of Education, China. He has worked in the areas of data mining and computer architecture.

Lixin Liu received his BS degree in software engineering from Hebei University of Technology, China in 2012 and is currently a master student in Beijing University of Posts and Telecommunications (BUPT), China. His research interest is data mining. He is currently working on large scale graph mining.

Shouyou Song received his BS degree in computer science and technology from Huaqiao University, China in 2012 and is currently a master student in Beijing University of Posts and Telecommunications (BUPT), China. His research interest is complex networks and data mining. He is currently working on community detection in social networks.

Binxing Fang is a member of the Chinese Academy of Engineering and a professor in School of Computer Science at Beijing University of Posts and Telecommunications (BUPT), China. He received his PhD from Harbin Institute of Technology, China in 1989. He is currently the chief scientist of State Key Development Program of Basic Research of China, with the name Basic Research of Social Network Analysis and Network Information Diffusion. His current interests include social network analysis, IOT search, and information security.

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Su, Y., Zhang, X., Liu, L. et al. Understanding information interactions in diffusion: an evolutionary game-theoretic perspective. Front. Comput. Sci. 10, 518–531 (2016). https://doi.org/10.1007/s11704-015-5008-y

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