The Experts below are selected from a list of 157062 Experts worldwide ranked by ideXlab platform
K. J. Ray Liu - One of the best experts on this subject based on the ideXlab platform.
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GLOBECOM - Evolutionary social Information Diffusion analysis
2014 IEEE Global Communications Conference, 2014Co-Authors: Chunxiao Jiang, Yan Chen, K. J. Ray LiuAbstract:Nowadays, social networks are extremely large-scale with tremendous Information flows, where understanding how the Information diffuse over social networks becomes an important research issue. Most of the existing works on Information Diffusion analysis are based on either network structure modeling or empirical approach with dataset mining. However, the Information Diffusion is also heavily influenced by network users' decisions, actions and their socio-economic connections, which is generally ignored by existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic Information Diffusion process in social networks. To verify our theoretical analysis, we conduct experiments by using Facebook network and real-world Information spreading dataset of Memetracker. Experiment results show that the proposed game theoretic framework is effective and practical in modeling the social network users' Information forwarding behaviors.
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ICASSP - Modeling Information Diffusion dynamics over social networks
2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014Co-Authors: Chunxiao Jiang, Yan Chen, K. J. Ray LiuAbstract:Information Diffusion over social networks becomes a hot topic recently. Most of the existing works are based on the machine learning method with social network structure analysis and empirical data mining. However, the results learned from some specific dataset may not apply to the future networks, since the social network structure is in a highly dynamic environment. Moreover, the dynamics of Information Diffusion are also heavily influenced by network users’ decisions, actions and their socio-economic interactions, which is generally ignored by existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic Information Diffusion process in social networks, which focuses on the users’ behavior analysis from a microeconomics points of view. We also conduct experiments by using real-world Twitter Information Diffusion dataset, which shows that the proposed evolutionary game theoretic model is effective and practical in modeling the social network users’ Information Diffusion dynamics.
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Evolutionary Dynamics of Information Diffusion Over Social Networks
IEEE Transactions on Signal Processing, 2014Co-Authors: Chunxiao Jiang, Yan Chen, K. J. Ray LiuAbstract:Current social networks are of extremely large-scale generating tremendous Information flows at every moment. How Information diffuses 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 network users' decisions, actions, and socio-economic interactions are 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 nonuniform degree networks, with the highlight of two special networks, the Erdos-Renyi random network and the Barabasi-Albert scale-free network. We find that the dynamics of Information Diffusion over these three kinds of networks are scale-free and all the three dynamics are 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 an experiment on a Twitter hashtags dataset, which shows that the proposed game theoretic model can well fit and predict the Information Diffusion over real social networks.
Chunxiao Jiang - One of the best experts on this subject based on the ideXlab platform.
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graphical evolutionary game for Information Diffusion over social networks
IEEE Journal of Selected Topics in Signal Processing, 2014Co-Authors: Chunxiao Jiang, Yan Chen, K Ray J LiuAbstract:Social networks have become ubiquitous in our daily life, as such they have attracted great research interests recently. A key challenge is that it is of extremely large-scale with tremendous Information flow, creating the phenomenon of “Big Data.” Under such a circumstance, understanding Information Diffusion over social networks has become an important research issue. Most of the existing works on Information Diffusion analysis are based on either network structure modeling or empirical approach with dataset mining. However, the Information Diffusion is also heavily influenced by network users' decisions, actions and their socio-economic connections, which is generally ignored in existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic Information Diffusion process in social networks. Specifically, we analyze the framework in uniform degree and non-uniform degree networks and derive the closed-form expressions of the evolutionary stable network states. Moreover, the Information Diffusion over two special networks, Erdos-Renyi random network and the Barabasi-Albert scale-free network, are also highlighted. To verify our theoretical analysis, we conduct experiments by using both synthetic networks and real-world Facebook network, as well as real-world Information spreading dataset of Memetracker. Experiments shows that the proposed game theoretic framework is effective and practical in modeling the social network users' Information forwarding behaviors.
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GLOBECOM - Evolutionary social Information Diffusion analysis
2014 IEEE Global Communications Conference, 2014Co-Authors: Chunxiao Jiang, Yan Chen, K. J. Ray LiuAbstract:Nowadays, social networks are extremely large-scale with tremendous Information flows, where understanding how the Information diffuse over social networks becomes an important research issue. Most of the existing works on Information Diffusion analysis are based on either network structure modeling or empirical approach with dataset mining. However, the Information Diffusion is also heavily influenced by network users' decisions, actions and their socio-economic connections, which is generally ignored by existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic Information Diffusion process in social networks. To verify our theoretical analysis, we conduct experiments by using Facebook network and real-world Information spreading dataset of Memetracker. Experiment results show that the proposed game theoretic framework is effective and practical in modeling the social network users' Information forwarding behaviors.
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ICASSP - Modeling Information Diffusion dynamics over social networks
2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014Co-Authors: Chunxiao Jiang, Yan Chen, K. J. Ray LiuAbstract:Information Diffusion over social networks becomes a hot topic recently. Most of the existing works are based on the machine learning method with social network structure analysis and empirical data mining. However, the results learned from some specific dataset may not apply to the future networks, since the social network structure is in a highly dynamic environment. Moreover, the dynamics of Information Diffusion are also heavily influenced by network users’ decisions, actions and their socio-economic interactions, which is generally ignored by existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic Information Diffusion process in social networks, which focuses on the users’ behavior analysis from a microeconomics points of view. We also conduct experiments by using real-world Twitter Information Diffusion dataset, which shows that the proposed evolutionary game theoretic model is effective and practical in modeling the social network users’ Information Diffusion dynamics.
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Evolutionary Dynamics of Information Diffusion Over Social Networks
IEEE Transactions on Signal Processing, 2014Co-Authors: Chunxiao Jiang, Yan Chen, K. J. Ray LiuAbstract:Current social networks are of extremely large-scale generating tremendous Information flows at every moment. How Information diffuses 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 network users' decisions, actions, and socio-economic interactions are 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 nonuniform degree networks, with the highlight of two special networks, the Erdos-Renyi random network and the Barabasi-Albert scale-free network. We find that the dynamics of Information Diffusion over these three kinds of networks are scale-free and all the three dynamics are 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 an experiment on a Twitter hashtags dataset, which shows that the proposed game theoretic model can well fit and predict the Information Diffusion over real social networks.
Yong-yeol Ahn - One of the best experts on this subject based on the ideXlab platform.
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Optimal Network Modularity for Information Diffusion
Physical review letters, 2014Co-Authors: Azadeh Nematzadeh, Emilio Ferrara, Alessandro Flammini, Yong-yeol AhnAbstract:We investigate the impact of community structure on Information Diffusion with the linear threshold model. Our results demonstrate that modular structure may have counterintuitive effects on Information Diffusion when social reinforcement is present. We show that strong communities can facilitate global Diffusion by enhancing local, intracommunity spreading. Using both analytic approaches and numerical simulations, we demonstrate the existence of an optimal network modularity, where global Diffusion requires the minimal number of early adopters.
Yan Chen - One of the best experts on this subject based on the ideXlab platform.
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graphical evolutionary game for Information Diffusion over social networks
IEEE Journal of Selected Topics in Signal Processing, 2014Co-Authors: Chunxiao Jiang, Yan Chen, K Ray J LiuAbstract:Social networks have become ubiquitous in our daily life, as such they have attracted great research interests recently. A key challenge is that it is of extremely large-scale with tremendous Information flow, creating the phenomenon of “Big Data.” Under such a circumstance, understanding Information Diffusion over social networks has become an important research issue. Most of the existing works on Information Diffusion analysis are based on either network structure modeling or empirical approach with dataset mining. However, the Information Diffusion is also heavily influenced by network users' decisions, actions and their socio-economic connections, which is generally ignored in existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic Information Diffusion process in social networks. Specifically, we analyze the framework in uniform degree and non-uniform degree networks and derive the closed-form expressions of the evolutionary stable network states. Moreover, the Information Diffusion over two special networks, Erdos-Renyi random network and the Barabasi-Albert scale-free network, are also highlighted. To verify our theoretical analysis, we conduct experiments by using both synthetic networks and real-world Facebook network, as well as real-world Information spreading dataset of Memetracker. Experiments shows that the proposed game theoretic framework is effective and practical in modeling the social network users' Information forwarding behaviors.
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GLOBECOM - Evolutionary social Information Diffusion analysis
2014 IEEE Global Communications Conference, 2014Co-Authors: Chunxiao Jiang, Yan Chen, K. J. Ray LiuAbstract:Nowadays, social networks are extremely large-scale with tremendous Information flows, where understanding how the Information diffuse over social networks becomes an important research issue. Most of the existing works on Information Diffusion analysis are based on either network structure modeling or empirical approach with dataset mining. However, the Information Diffusion is also heavily influenced by network users' decisions, actions and their socio-economic connections, which is generally ignored by existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic Information Diffusion process in social networks. To verify our theoretical analysis, we conduct experiments by using Facebook network and real-world Information spreading dataset of Memetracker. Experiment results show that the proposed game theoretic framework is effective and practical in modeling the social network users' Information forwarding behaviors.
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ICASSP - Modeling Information Diffusion dynamics over social networks
2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014Co-Authors: Chunxiao Jiang, Yan Chen, K. J. Ray LiuAbstract:Information Diffusion over social networks becomes a hot topic recently. Most of the existing works are based on the machine learning method with social network structure analysis and empirical data mining. However, the results learned from some specific dataset may not apply to the future networks, since the social network structure is in a highly dynamic environment. Moreover, the dynamics of Information Diffusion are also heavily influenced by network users’ decisions, actions and their socio-economic interactions, which is generally ignored by existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic Information Diffusion process in social networks, which focuses on the users’ behavior analysis from a microeconomics points of view. We also conduct experiments by using real-world Twitter Information Diffusion dataset, which shows that the proposed evolutionary game theoretic model is effective and practical in modeling the social network users’ Information Diffusion dynamics.
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Evolutionary Dynamics of Information Diffusion Over Social Networks
IEEE Transactions on Signal Processing, 2014Co-Authors: Chunxiao Jiang, Yan Chen, K. J. Ray LiuAbstract:Current social networks are of extremely large-scale generating tremendous Information flows at every moment. How Information diffuses 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 network users' decisions, actions, and socio-economic interactions are 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 nonuniform degree networks, with the highlight of two special networks, the Erdos-Renyi random network and the Barabasi-Albert scale-free network. We find that the dynamics of Information Diffusion over these three kinds of networks are scale-free and all the three dynamics are 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 an experiment on a Twitter hashtags dataset, which shows that the proposed game theoretic model can well fit and predict the Information Diffusion over real social networks.
Ruilin Zhang - One of the best experts on this subject based on the ideXlab platform.
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Users’ mobility enhances Information Diffusion in online social networks
Information Sciences, 2021Co-Authors: Yanan Wang, Jun Wang, Haiying Wang, Ruilin ZhangAbstract:Abstract Online social networks have gradually changed the way that people exchange Information as increasingly more people spread Information via social networks. Most of the prior literature about propagation dynamics stresses static networks. Actually, owing to the openness of the network environment, users can freely enter and leave social networks. Therefore, we consider users’ mobility and establish the Information Diffusion model called the in-out-Unacquired-Acquired-Rejected in online social networks. Specifically, we derive the Information Diffusion system using mean field theory. From theoretical analysis, the propagation threshold R 0 of the Information Diffusion system is obtained. We prove that if R 0 1 , then the Information-free equilibrium of the model is globally asymptotically stable and if R 0 > 1 , then the Information is permanently diffused. By means of numerical simulations using Sina Weibo, this paper verifies the theoretical analysis and the simulation results show that the larger the value of R 0 , the better the Information Diffusion effect in online social networks. Moreover, users’ mobility increases the connections among users and further expands the Information Diffusion. In addition, comparative experiments with the susceptible-infected-recovered (SIR) model also illustrate the applicability of our Information Diffusion model.
