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Multi-Objective Optimization for Relevant Sub-graph Extraction

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Learning and Intelligent Optimization (LION 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7997))

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Abstract

In recent years, graph clustering methods have rapidly emerged to mine latent knowledge and functions in networks. Most sub-graphs extracting methods that have been introduced fall into graph clustering. In this paper, a novel trend of relevant sub-graphs extraction problem was considered as multi-objective optimization. Genetic Algorithms (GAs) and Simulated Annealing (SA) were then used to solve the problem applied to biological networks. Comparisons between GAs, SA and Markov Cluster Algorithm (MCL) were carried out and the results showed that the proposed approach is superior.

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Acknowledgment

We thank F. Radvanyi for fruitful discussions and the anonymous referees for their pertinent suggestions.This work is supported by the INCa (French National Institute of Cancer) through the INCa project PL-2010-196. R. Nicolle is supported by a fellowship from the French Ministry of Higher Education and Research.

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Authors and Affiliations

  1. iSSB CNRS UPS3509, University of Evry-Val-dEssonne EA4527, Genopole Campus 1, 5 Rue Henri Desbruères, 91030, Evry CEDEX, France

    Mohamed Elati, Cuong To & Rémy Nicolle

Authors
  1. Mohamed Elati
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  2. Cuong To
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  3. Rémy Nicolle
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Corresponding author

Correspondence to Rémy Nicolle .

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Editors and Affiliations

  1. Università Catania Dipto. Matematica e Informatica, Catania, Italy

    Giuseppe Nicosia

  2. Industrial & Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos Pardalos

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Elati, M., To, C., Nicolle, R. (2013). Multi-Objective Optimization for Relevant Sub-graph Extraction. In: Nicosia, G., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2013. Lecture Notes in Computer Science(), vol 7997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44973-4_12

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  • DOI: https://doi.org/10.1007/978-3-642-44973-4_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-44972-7

  • Online ISBN: 978-3-642-44973-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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