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A Novel Approach to Evaluate Community Detection Algorithms on Ground Truth

  • Giulio Rossetti
  • Luca Pappalardo
  • Salvatore Rinzivillo
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 644)

Abstract

Evaluating a community detection algorithm is a complex task due to the lack of a shared and universally accepted definition of community. In literature, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i.e., Normalized Mutual Information). In this paper we propose a novel approach aimed at evaluating the adherence of a community partition to the ground truth: our methodology provides more information than the state-of-the-art ones and is fast to compute on large-scale networks. We evaluate its correctness by applying it to six popular community detection algorithms on four large-scale network datasets. Experimental results show how our approach allows to easily evaluate the obtained communities on the ground truth and to characterize the quality of community detection algorithms.

Keywords

Ground Truth Community Detection Normalize Mutual Information Community Evaluation Community Detection Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was partially funded by the European Community’s H2020 Program under the funding scheme “FETPROACT-1-2014: Global Systems Science (GSS)”, grant agreement #641191 CIMPLEX “Bringing CItizens, Models and Data together in Participatory, Interactive SociaL EXploratories”, https://www.cimplex-project.eu. Our research is also supported by the European Community’s H2020 Program under the scheme “INFRAIA-1-2014-2015: Research Infrastructures”, grant agreement #654024 “SoBigData: Social Mining & Big Data Ecosystem”, http://www.sobigdata.eu.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Giulio Rossetti
    • 1
  • Luca Pappalardo
    • 1
  • Salvatore Rinzivillo
    • 1
  1. 1.Institute of Information Science and Technologies (ISTI)National Research Council of Italy (CNR)PisaItaly

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