Advertisement

Bringing a Feature Selection Metric from Machine Learning to Complex Networks

  • Nicolas Dugué
  • Jean-Charles Lamirel
  • Anthony Perez
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 813)

Abstract

Introduced in the context of machine learning, the Feature F-measure is a statistical feature selection metric without parameters that allows to describe classes through a set of salient features. It was shown efficient for classification, cluster labeling and clustering model quality measurement. In this paper, we introduce the Node F-measure, its transposition in the context of networks, where it can by analogy be applied to detect salient nodes in communities. This approach benefits from the parameter-free system of Feature F-Measure, its low computational complexity and its well-evaluated performance. Interestingly, we show that in addition to these properties, Node F-measure is correlated with certain centrality measures, and with measures designed to characterize the community roles of nodes. We also observe that the usual community roles measures are strongly dependent from the size of the communities whereas the ones we propose are by definition linked to the density of the community. This hence makes their results comparable from one network to another. Finally, the parameter-free selection process applied to nodes allows for a universal system, contrary to the thresholds previously defined empirically for the establishment of community roles. These results may have applications regarding leadership in scientific communities or when considering temporal monitoring of communities.

References

  1. 1.
    Barabási, A.L.: Network Science. Cambridge University Press, Cambridge (2016)zbMATHGoogle Scholar
  2. 2.
    Brin, S., Page, L.: Reprint of: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. 56(18), 3825–3833 (2012)CrossRefGoogle Scholar
  3. 3.
    Dugué, N., Labatut, V., Perez, A.: A community role approach to assess social capitalists visibility in the twitter network. Soc. Netw. Anal. Min. 5(1), 1–13 (2015)CrossRefGoogle Scholar
  4. 4.
    Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1979)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Guimerà, R., Amaral, L.: Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005)CrossRefGoogle Scholar
  6. 6.
    Klimm, F., Borge-Holthoefer, J., Wessel, N., Kurths, J., Zamora-López, G.: Individual node’s contribution to the mesoscale of complex networks. New J. Phys. 16(12), 125006 (2014)CrossRefGoogle Scholar
  7. 7.
    Kunegis, J.: Konect: the koblenz network collection. In: WWW, pp. 1343–1350 (2013)Google Scholar
  8. 8.
    Lamirel, J.C., Cuxac, P., Chivukula, A., Hajlaoui, K.: Optimizing text classification through efficient feature selection based on quality metric. J. I. IS 45(3), 379–396 (2015)Google Scholar
  9. 9.
    Lamirel, J.C., Dugué, N., Cuxac, P.: New efficient clustering quality indexes. In: IJCNN (2016)Google Scholar
  10. 10.
    Lamirel, J.C., Falk, I., Gardent, C.: Federating clustering and cluster labelling capabilities with a single approach based on feature maximization: French verb classes identification with igngf. Neurocomputing 147, 136–146 (2015)CrossRefGoogle Scholar
  11. 11.
    Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 046110 (2008)CrossRefGoogle Scholar
  12. 12.
    Lancichinetti, A., Kivelä, M., Saramäki, J., Fortunato, S.: Characterizing the community structure of complex networks. PloS one 5(8), e11976 (2010)CrossRefGoogle Scholar
  13. 13.
    Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. 69(2), 026113 (2004)Google Scholar
  14. 14.
    Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. 105(4), 1118–1123 (2008)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nicolas Dugué
    • 1
  • Jean-Charles Lamirel
    • 2
  • Anthony Perez
    • 3
  1. 1.Le Mans Université, LIUM EA 4023Laboratoire d’Informatique de l’Université du MansLe Mans Cedex 9France
  2. 2.Université de Strasbourg, LORIA, Equipe SynalpStrasbourgFrance
  3. 3.Univ. Orléans, INSA Centre Val de Loire, LIFO EA 4022OrléansFrance

Personalised recommendations