Multi-Objective Optimization for Relevant Sub-graph Extraction

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7997)

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.

Keywords

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

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

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

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