Trend Mining in Social Networks: A Study Using a Large Cattle Movement Database

  • Puteri N. E. Nohuddin
  • Rob Christley
  • Frans Coenen
  • Christian Setzkorn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6171)


This paper reports on a mechanism to identify temporal spatial trends in social networks. The trends of interest are defined in terms of the occurrence frequency of time stamped patterns across social network data. The paper proposes a technique for identifying such trends founded on the Frequent Pattern Mining paradigm. The challenge of this technique is that, given appropriate conditions, many trends may be produced; and consequently the analysis of the end result is inhibited. To assist in the analysis, a Self Organising Map (SOM) based approach, to visualizing the outcomes, is proposed. The focus for the work is the social network represented by the UK’s cattle movement data base. However, the proposed solution is equally applicable to other large social networks.


Social Network Analysis Trend Mining Trend Visualization 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Puteri N. E. Nohuddin
    • 1
  • Rob Christley
    • 2
  • Frans Coenen
    • 1
  • Christian Setzkorn
    • 2
  1. 1.Department of Computer ScienceUniversity of LiverpoolUK
  2. 2.School of Veterinary ScienceUniversity of Liverpool and National Centre for Zoonosis ResearchLeahurst, NestonUK

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