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https://hdl.handle.net/1822/38054
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Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Di Jin | por |
dc.contributor.author | Dongxiao He | por |
dc.contributor.author | Dayou Liu | por |
dc.contributor.author | Baquero, Carlos | por |
dc.date.accessioned | 2015-11-09T11:29:21Z | - |
dc.date.available | 2015-11-09T11:29:21Z | - |
dc.date.issued | 2010 | - |
dc.identifier.isbn | 978-1-4244-8817-9 | - |
dc.identifier.issn | 1082-3409 | por |
dc.identifier.uri | https://hdl.handle.net/1822/38054 | - |
dc.description.abstract | Detecting communities from complex networks has triggered considerable attention in several application domains. Targeting this problem, a local search based genetic algorithm (GALS) which employs a graph-based representation (LAR) has been proposed in this work. The core of the GALS is a local search based mutation technique. Aiming to overcome the drawbacks of the existing mutation methods, a concept called marginal gene has been proposed, and then an effective and efficient mutation method, combined with a local search strategy which is based on the concept of marginal gene, has also been proposed by analyzing the modularity function. Moreover, in this paper the percolation theory on ER random graphs is employed to further clarify the effectiveness of LAR presentation; A Markov random walk based method is adopted to produce an accurate and diverse initial population; the solution space of GALS will be significantly reduced by using a graph based mechanism. The proposed GALS has been tested on both computer-generated and real-world networks, and compared with some competitive community mining algorithms. Experimental result has shown that GALS is hig y effective and efficient for discovering community structure. | por |
dc.description.sponsorship | This work was supported by National Natural Science Foundation of China under Grant Nos. 60873149, 60973088, National High-Tech Research and Development Plan of China under Grant No. 2006AA10Z245, Open Project Program of the National Laboratory of Pattern Recognition, and BRIDGING THE GAP Erasmus Mundus project of EU. We would like to thank Mark Newman for providing us with the source code of algorithms FN and GN, and some real-world network data. | por |
dc.language.iso | eng | por |
dc.publisher | IEEE | por |
dc.rights | openAccess | por |
dc.subject | Complex network | por |
dc.subject | Community mining | por |
dc.subject | Network clustering | por |
dc.subject | Genetic algorithm | por |
dc.subject | Local search | por |
dc.subject | network | por |
dc.title | Genetic algorithm with local search for community mining in complex networks | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5670026 | por |
sdum.publicationstatus | published | por |
oaire.citationStartPage | 105 | por |
oaire.citationEndPage | 112 | por |
oaire.citationConferencePlace | Arras | por |
oaire.citationTitle | 22nd International Conference on Tools with Artificial Intelligence | por |
oaire.citationVolume | 1 | por |
dc.publisher.uri | IEEE | por |
dc.identifier.doi | 10.1109/ICTAI.2010.23 | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Proceedings-International Conference on Tools With Artificial Intelligence | por |
sdum.conferencePublication | 22nd International Conference on Tools with Artificial Intelligence | por |
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