Nonmetric MDS Consensus Community Detection

Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Community detection methods for the analysis of complex networks are increasingly important in modern literature. At the same time it is still an open problem. The approach proposed in this work is to adopt an ensemble procedure for obtaining a consensus matrix from which to perform a nonmetric MDS approach and then a clustering algorithm which allows to get a consensus partition of the nodes. The simulation study offers some interesting insights on the procedure because it shows that it is possible to understand the key nodes and the stable communities by considering different algorithms. The proposed approach is still applied to real data related to a network of patents.

Keywords

Community detection Complex networks Nonmetric multidimensional scaling 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

The authors wish to thank Ivan Cucco for proving the data related to the joint patent application network.

References

  1. 1.
    Balzanella, A., Verde, R.: Summarizing and detecting structural drifts from multiple data streams. In: Giusti, A., Ritter, G., Vichi, M. (eds.) Classification and Data Mining, vol. XVIII, 26, pp. 105–112. Springer, Berlin (2013)Google Scholar
  2. 2.
    Barthelemy, M.: Betweenness centrality in large complex networks. Eur. Phys. J. B Condensed Matter Complex Syst. 38(2), 163–168 (2004)CrossRefGoogle Scholar
  3. 3.
    Csardi G., Nepusz T.: The igraph software package for complex network research. Int. J. Complex Syst. 1695, 1–9 (2006)Google Scholar
  4. 4.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Kruskal, J.B.: Nonmetric multidimensional scaling: a numerical method. Psychometrika 29, 115–129 (1964)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Leskovec, J., Lang, K.J., Mahoney, M.: Empirical comparison of algorithms for network community detection. In: Proceedings of the 19th International Conference on World Wide Web, pp. 631–640. ACM, New York (2010)Google Scholar
  7. 7.
    Newman, M.E.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Strehl, A., Ghosh J.: Cluster ensembles a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2002)MathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Università degli Studi “Niccolò Cusano” Telematica RomaRomeItaly
  2. 2.Department of Political ScienceSecond University of NaplesCasertaItaly

Personalised recommendations