Analysing information flows and key mediators through temporal centrality metrics

John Tang, Mirco Musolesi, Cecilia Mascolo, Vito Latora, Vincenzo Nicosia

Research output: Chapter in Book/Report/Conference proceedingOther contribution

142 Citations (Scopus)

Abstract

The study of influential members of human networks is an important research question in social network analysis. However, the current state-of-the-art is based on static or aggregated representation of the network topology. We argue that dynamically evolving network topologies are inherent in many systems, including real online social and technological networks: fortunately the nature of these systems is such that they allow the gathering of large quantities of finegrained 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 important 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 measure 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.
Original languageEnglish
Title of host publicationProceedings of the 3rd Workshop on Social Network Systems (SNS'10)
PublisherACM
Pages1-6
Number of pages6
ISBN (Print)978-1-4503-0080-3
DOIs
Publication statusPublished - 2010

Keywords

  • complex networks, information diffusion, key mediators, social networks, temporal centrality, temporal graphs, temporal metrics

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