中国物理B ›› 2016, Vol. 25 ›› Issue (10): 108904-108904.doi: 10.1088/1674-1056/25/10/108904

• INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY • 上一篇    

Subtle role of latency for information diffusion in online social networks

Fei Xiong(熊菲), Xi-Meng Wang(王夕萌), Jun-Jun Cheng(程军军)   

  1. 1 School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China;
    2 Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China;
    3 China Information Technology Security Evaluation Center, Beijing 100085, China
  • 收稿日期:2016-04-14 修回日期:2016-05-19 出版日期:2016-10-05 发布日期:2016-10-05
  • 通讯作者: Fei Xiong E-mail:xiongf@bjtu.edu.cn
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 61401015 and 61271308), the Fundamental Research Funds for the Central Universities, China (Grant No. 2014JBM018), and the Talent Fund of Beijing Jiaotong University, China (Grant No. 2015RC013).

Subtle role of latency for information diffusion in online social networks

Fei Xiong(熊菲)1,2, Xi-Meng Wang(王夕萌)1,2, Jun-Jun Cheng(程军军)3   

  1. 1 School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China;
    2 Key Laboratory of Communication and Information Systems, Beijing Municipal Commission of Education, Beijing Jiaotong University, Beijing 100044, China;
    3 China Information Technology Security Evaluation Center, Beijing 100085, China
  • Received:2016-04-14 Revised:2016-05-19 Online:2016-10-05 Published:2016-10-05
  • Contact: Fei Xiong E-mail:xiongf@bjtu.edu.cn
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant Nos. 61401015 and 61271308), the Fundamental Research Funds for the Central Universities, China (Grant No. 2014JBM018), and the Talent Fund of Beijing Jiaotong University, China (Grant No. 2015RC013).

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摘要： Information diffusion in online social networks is induced by the event of forwarding information for users, and latency exists widely in user spreading behaviors. Little work has been done to reveal the effect of latency on the diffusion process. In this paper, we propose a propagation model in which nodes may suspend their spreading actions for a waiting period of stochastic length. These latent nodes may recover their activity again. Meanwhile, the mechanism of forwarding information is also introduced into the diffusion model. Mean-field analysis and numerical simulations indicate that our model has three nontrivial results. First, the spreading threshold does not correlate with latency in neither homogeneous nor heterogeneous networks, but depends on the spreading and refractory parameter. Furthermore, latency affects the diffusion process and changes the infection scale. A large or small latency parameter leads to a larger final diffusion extent, but the intrinsic dynamics is different. Large latency implies forwarding information rapidly, while small latency prevents nodes from dropping out of interactions. In addition, the betweenness is a better descriptor to identify influential nodes in the model with latency, compared with the coreness and degree. These results are helpful in understanding some collective phenomena of the diffusion process and taking measures to restrain a rumor in social networks.

关键词: information diffusion, node latency, user behavior, complex networks

Abstract: Information diffusion in online social networks is induced by the event of forwarding information for users, and latency exists widely in user spreading behaviors. Little work has been done to reveal the effect of latency on the diffusion process. In this paper, we propose a propagation model in which nodes may suspend their spreading actions for a waiting period of stochastic length. These latent nodes may recover their activity again. Meanwhile, the mechanism of forwarding information is also introduced into the diffusion model. Mean-field analysis and numerical simulations indicate that our model has three nontrivial results. First, the spreading threshold does not correlate with latency in neither homogeneous nor heterogeneous networks, but depends on the spreading and refractory parameter. Furthermore, latency affects the diffusion process and changes the infection scale. A large or small latency parameter leads to a larger final diffusion extent, but the intrinsic dynamics is different. Large latency implies forwarding information rapidly, while small latency prevents nodes from dropping out of interactions. In addition, the betweenness is a better descriptor to identify influential nodes in the model with latency, compared with the coreness and degree. These results are helpful in understanding some collective phenomena of the diffusion process and taking measures to restrain a rumor in social networks.

Key words: information diffusion, node latency, user behavior, complex networks

中图分类号:  (Complex systems)

  • 89.75.-k
87.23.Ge (Dynamics of social systems) 89.75.Fb (Structures and organization in complex systems) 05.10.-a (Computational methods in statistical physics and nonlinear dynamics)