Graphical Evolutionary Game for Information Diffusion Over Social Networks

Abstract

Abstract-Current social networks are of extremely largescale generating tremendous information flows at every moment. How information diffuse over social networks has attracted much attention from both industry and academics. Most of the existing works on information diffusion analysis are based on machine learning methods focusing on social network structure analysis and empirical data mining. However, the dynamics of information diffusion, which are heavily influenced by network users' decisions, actions and their socio-economic interactions, is generally ignored by most of existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we derive the information diffusion dynamics in complete networks, uniform degree and non-uniform degree networks, with the highlight of two special networks, Erdős-Rényi random network and the Barabási-Albert scale-free network. We find that the dynamics of information diffusion over these three kinds of networks are scale-free and the same with each other when the network scale is sufficiently large. To verify our theoretical analysis, we perform simulations for the information diffusion over synthetic networks and real-world Facebook networks. Moreover, we also conduct experiment on Twitter hashtags dataset, which shows that the proposed game theoretic model can well fit and predict the information diffusion over real social networks.

Information Credibility Modeling in Cooperative Networks: Equilibrium and Mechanism Design

“…In the following, we will rely on the evolutionary equilibrium theory [28] for modelling the credibility equilibrium of the information sharing in a cooperative network. Some examples of evolutionary game theory in network modelling include the information diffusion [29], network selection [30], cognitive radio networks [31], [32] adaptive filtering networks [33], P2P steaming [34] and social networks [35].…”

Evolutionary Dynamics of Information Diffusion Over Social Networks

“…In essence, the traditional evolutionary game can be regarded as a special case of graphical EGT, where the corresponding graph structure is complete. Previously, we have used graphical EGT to model the adaptive networks [29], as well as the stable state of information diffusion over social networks [37]. The major difference is that, we focus on the dynamics analysis of information diffusion in this paper using replicator dynamics, while [37] focused on the final stable state of information diffusion by analyzing the evolutionarily stable state (ESS), which is also an important concept in the EGT.…”

“…However, the scale-free property may not hold due to the term , which is the expectation of and may contain the network scale information. Moreover, in [37], we analyzed the evolutionary stable states of information diffusion under IM strategy update rule. Here, in Proposition 4, we can also find three stable states 0, 1 and in (25), which is consistent with the results in [37].…”

“…In this experiment, we compare our results with one of the most related existing works [43] using data mining method, in which the dynamics of information diffusion are predicted by the following model (37) where the parameters , and can also be estimated through least-squares curve fitting in a similar way. Fig.…”

Efficient secure outsourcing of large-scale linear systems of equations

“…For example, social networks need to monitor and record interactions among millions or billions of users [1] [2]; scientists need to sequence the genome of complex organisms [3]; and power system operators collect enormous amounts of data from the electric grid for real-time monitoring and offline analysis [4]. Due to advances in computer memory, large-scale data sets can be stored in a cost-effective manner.…”