Multi-Objective Optimization for Relevant Sub-graph Extraction

  • Mohamed Elati
  • Cuong To
  • Rémy NicolleEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7997)


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.


Sub-graph extraction Genetic algorithms Simulated annealing Multi-objective optimization 



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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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.iSSB CNRS UPS3509University of Evry-Val-dEssonne EA4527Evry CEDEXFrance

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