Mixing local and global information for community detection in large networks

  • Authors:

  • Pasquale De Meo;Emilio Ferrara;Giacomo Fiumara;Alessandro Provetti

  • Affiliations:

  • University of Messina, Department of Mathematics and Informatics, V.le F. Stagno DAlcontres 31, I-98166 Messina, Italy;Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University Bloomington, 919 E. 10th St., Bloomington, IN 47408, USA and University of Messina, Depart ...;University of Messina, Department of Mathematics and Informatics, V.le F. Stagno DAlcontres 31, I-98166 Messina, Italy;University of Messina, Department of Mathematics and Informatics, V.le F. Stagno DAlcontres 31, I-98166 Messina, Italy and Oxford-Man Institute of Quantitative Finance, University of Oxford, Oxfor ...

  • Venue:
  • Journal of Computer and System Sciences
  • Year:
  • 2014

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Abstract

Clustering networks play a key role in many scientific fields, from Biology to Sociology and Computer Science. Some clustering approaches are called global because they exploit knowledge about the whole network topology. Vice versa, so-called local methods require only a partial knowledge of the network topology. Global approaches yield accurate results but do not scale well on large networks; local approaches, vice versa, are less accurate but computationally fast. We propose CONCLUDE (COmplex Network CLUster DEtection), a new clustering method that couples the accuracy of global approaches with the scalability of local methods. CONCLUDE generates random, non-backtracking walks of finite length to compute the importance of each edge in keeping the network connected, i.e., its edge centrality. Edge centralities allow for mapping vertices onto points of a Euclidean space and compute all-pairs distances between vertices; those distances are then used to partition the network into clusters.