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Finding maximal homogeneous clique sets

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Abstract

Many datasets can be encoded as graphs with sets of labels associated with the vertices. We consider this kind of graphs and we propose to look for patterns called maximal homogeneous clique sets, where such a pattern is a subgraph that is structured in several large cliques and where all vertices share enough labels. We present an algorithm based on graph enumeration to compute all patterns satisfying user-defined constraints on the number of separated cliques, on the size of these cliques, and on the number of labels shared by all the vertices. Our approach is tested on real datasets based on a social network of scientific collaborations and on a biological network of protein–protein interactions. The experiments show that the patterns are useful to exhibit subgraphs organized in several core modules of interactions. Performances are reported on real data and also on synthetic ones, showing that the approach can be applied on different kinds of large datasets.

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Notes

  1. However, this does not require that cliques in \(M\) are maximal cliques in the whole graph \(\mathcal{G }\).

  2. For sake of simplicity in the examples, a clique is simply denoted by a sequence of letters representing the clique vertices.

  3. However, if needed, a simple post-processing can be used to remove these small cliques.

  4. http://www.informatik.uni-trier.de/~ley/db/.

  5. http://string-db.org/, snapshot of November 2009.

  6. http://bsmc.insa-lyon.fr/squat/.

  7. This confidence is a measure provided by STRING. A high confidence means that there are strong evidences that the interaction exists.

  8. http://www.genenames.org/.

  9. We retain the term list used in L2L, however these lists are simply sets.

  10. The category of the lists containing genes categorized under biological process in GeneOntology [1].

  11. Sensory perception of light stimulus is defined as the series of events required for an organism to receive a sensory light stimulus, convert it to a molecular signal, and recognize and characterize the signal.

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Acknowledgments

We would like to thank the anonymous referees for their constructive comments and useful suggestions. This work is partly funded by the Rhône-Alpes Complex Systems Institute (IXXI) through the project REHMI and by the French National Research Agency (ANR) through the projects FOSTER (ANR-2010-COSI-012) and BINGO2 (ANR-07-MDCO-014).

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Authors and Affiliations

  1. Université de Lyon, CNRS, INSA-Lyon, LIRIS, UMR5205, INRIA, 69621 , Lyon, France

    Pierre-Nicolas Mougel & Christophe Rigotti

  2. Université Lyon, CNRS, Université Lyon 1, LIRIS, UMR5205, 69622 , Lyon, France

    Marc Plantevit

  3. INRIA Université Lyon, Université Lyon 1, Centre de Génétique et de Physiologie Moléculaire et Cellulaire, (CGPhiMC), CNRS, UMR5534, 69622 , Lyon, France

    Olivier Gandrillon

Authors
  1. Pierre-Nicolas Mougel
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  2. Christophe Rigotti
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  3. Marc Plantevit
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  4. Olivier Gandrillon
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Correspondence to Christophe Rigotti.

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Mougel, PN., Rigotti, C., Plantevit, M. et al. Finding maximal homogeneous clique sets. Knowl Inf Syst 39, 579–608 (2014). https://doi.org/10.1007/s10115-013-0625-y

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