Seeking powerful information initial spreaders in online social networks: a dense group perspective

  • Songjun Ma
  • Ge Chen
  • Luoyi Fu
  • Weijie Wu
  • Xiaohua Tian
  • Jun Zhao
  • Xinbing Wang


The rapid growth of online social networks (OSNs) has ultimately facilitated information spreading and changed the economics of mobile networks. It is important to understand how to spread information as widely as possible. In this paper, we aim to seek powerful information initial spreaders with an efficient manner. We use the mean-field theory to characterize the process of information spreading based on the Susceptible Infected (SI) model and validate that the prevalence of information depends on the network density. Inspired by this result, we seek the initial spreaders from closely integrated groups of nodes, i.e., dense groups (DGs). In OSNs, DGs distribute dispersedly over the network, so our approach can be fulfilled in a distributed way by seeking the spreaders in each DG. We first design a DG Generating Algorithm to detect DGs, where nodes within the DG have more internal connections than external ones. Second, based on the detected DGs, we design a criterion to seek powerful initial spreaders from each DG. We conduct experiments as well as statistical analysis on real OSNs. The results show that our approach provides a satisfactory performance as well as computational efficiency.


Online social networks Information initial spreader Dense group Epidemic model 


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Songjun Ma
    • 1
  • Ge Chen
    • 1
  • Luoyi Fu
    • 1
  • Weijie Wu
    • 1
  • Xiaohua Tian
    • 1
  • Jun Zhao
    • 2
  • Xinbing Wang
    • 1
  1. 1.School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Electrical, Computer and Energy EngineeringArizona State UniversityTempeUSA

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