Testing Community Detection Algorithms: A Closer Look at Datasets

  • Ahmed Ibrahem Hafez
  • Aboul Ella Hassanien
  • Aly A. Fahmy
Part of the Intelligent Systems Reference Library book series (ISRL, volume 65)

Abstract

Social networks of various kinds demonstrate a strong community effect. Actors in a network tend to form closely-knit groups; those groups are also called communities or clusters. Detecting such groups in a social network (i.e., community detection) remains a core problem in social network analysis. Among the challenges that face the researchers to come up with advanced community detection methods, there is a key challenge, which is the validation and evaluation of their methods. The limited benchmark data available, the lack of ground truth for many of the available network datasets, and the nature of the social behavior factor in the problem, turned the evaluation process to be very hard. Accordingly, understanding such challenges may help in designing good community detection methods. This chapter presents testing strategies for community detection approaches and explores a number of datasets that could be used in the testing process as well as stating some characteristics of those datasets.

Keywords

Social network analysis Community detection Method evaluation Social network datasets 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ahmed Ibrahem Hafez
    • 1
  • Aboul Ella Hassanien
    • 2
  • Aly A. Fahmy
    • 3
  1. 1.Faculty of Computer and Information, Scientific Research Group in Egypt (SRGE)Minia UniversityMiniaEgypt
  2. 2.Faculty of Computers and Information, Scientific Research Group in Egypt (SRGE)Cairo UniversityCairoEgypt
  3. 3.Faculty of Computers and InformationCairo UniversityCairoEgypt

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