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DNE: A method for extracting cascaded diffusion networks from social networks

Eslami, M ; Sharif University of Technology | 2011

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  1. Type of Document: Article
  2. DOI: 10.1109/PASSAT/SocialCom.2011.85
  3. Publisher: 2011
  4. Abstract:
  5. The spread of information cascades over social networks forms the diffusion networks. The latent structure of diffusion networks makes the problem of extracting diffusion links difficult. As observing the sources of information is not usually possible, the only available prior knowledge is the infection times of individuals. We confront these challenges by proposing a new method called DNE to extract the diffusion networks by using the time-series data. We model the diffusion process on information networks as a Markov random walk process and develop an algorithm to discover the most probable diffusion links. We validate our model on both synthetic and real data and show the low dependency of our method to the number of transmitting cascades over the underlying networks. Moreover, The proposed model can speed up the extraction process up to 300 times with respect to the existing state of the art method
  6. Keywords:
  7. Diffusion networks ; Diffusion process ; Extraction process ; Information networks ; Latent structures ; Prior knowledge ; Random walk process ; Social Networks ; State-of-the-art methods ; Synthetic and real data ; Time-series data ; Underlying networks ; Information services ; Social networking (online) ; Social sciences computing ; Diffusion
  8. Source: Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011, 9 October 2011 through 11 October 2011 ; October , 2011 , Pages 41-48 ; 9780769545783 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6113093