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Publication Detail
Analysing information flows and key mediators through temporal centrality metrics
  • Publication Type:
    Conference
  • Authors:
    Tang J, Musolesi M, Mascolo C, Latora V, Nicosia V
  • Publication date:
    25/10/2010
  • Published proceedings:
    Proceedings of the 3rd Workshop on Social Network Systems, SNS'10
  • ISBN-13:
    9781450300803
  • Status:
    Published
Abstract
The study of inuential members of human networks is an important research question in social network analysis. How- ever, the current state-of-the-art is based on static or ag- gregated representation of the network topology. We argue that dynamically evolving network topologies are inherent in many systems, including real online social and techno- logical networks: fortunately the nature of these systems is such that they allow the gathering of large quantities of fine- grained temporal data on interactions amongst the network members. In this paper we propose novel temporal centrality metrics which take into account such dynamic interactions over time. Using a real corporate email dataset we evaluate the impor- tant individuals selected by means of static and temporal analysis taking two perspectives: firstly, from a semantic level, we investigate their corporate role in the organisation; and secondly, from a dynamic process point of view, we mea- sure information dissemination and the role of information mediators. We find that temporal analysis provides a better understanding of dynamic processes and a more accurate identification of important people compared to traditional static methods. Copyright 2010 ACM.
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Dept of Computer Science
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Dept of Computer Science
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