Efficient modularity density heuristics in graph clustering and their applications
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/164066 |
Resumo: | Modularity Density Maximization is a graph clustering problem which avoids the resolution limit degeneracy of the Modularity Maximization problem. This thesis aims at solving larger instances than current Modularity Density heuristics do, and show how close the obtained solutions are to the expected clustering. Three main contributions arise from this objective. The first one is about the theoretical contributions about properties of Modularity Density based prioritizers. The second one is the development of eight Modularity Density Maximization heuristics. Our heuristics are compared with optimal results from the literature, and with GAOD, iMeme-Net, HAIN, BMD- heuristics. Our results are also compared with CNM and Louvain which are heuristics for Modularity Maximization that solve instances with thousands of nodes. The tests were carried out by using graphs from the “Stanford Large Network Dataset Collection”. The experiments have shown that our eight heuristics found solutions for graphs with hundreds of thousands of nodes. Our results have also shown that five of our heuristics surpassed the current state-of-the-art Modularity Density Maximization heuristic solvers for large graphs. A third contribution is the proposal of six column generation methods. These methods use exact and heuristic auxiliary solvers and an initial variable generator. Comparisons among our proposed column generations and state-of-the-art algorithms were also carried out. The results showed that: (i) two of our methods surpassed the state-of-the-art algorithms in terms of time, and (ii) our methods proved the optimal value for larger instances than current approaches can tackle. Our results suggest clear improvements to the state-of-the-art results for the Modularity Density Maximization problem. |
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Santiago, Rafael deLamb, Luis da Cunha2017-07-18T02:32:24Z2017http://hdl.handle.net/10183/164066001026068Modularity Density Maximization is a graph clustering problem which avoids the resolution limit degeneracy of the Modularity Maximization problem. This thesis aims at solving larger instances than current Modularity Density heuristics do, and show how close the obtained solutions are to the expected clustering. Three main contributions arise from this objective. The first one is about the theoretical contributions about properties of Modularity Density based prioritizers. The second one is the development of eight Modularity Density Maximization heuristics. Our heuristics are compared with optimal results from the literature, and with GAOD, iMeme-Net, HAIN, BMD- heuristics. Our results are also compared with CNM and Louvain which are heuristics for Modularity Maximization that solve instances with thousands of nodes. The tests were carried out by using graphs from the “Stanford Large Network Dataset Collection”. The experiments have shown that our eight heuristics found solutions for graphs with hundreds of thousands of nodes. Our results have also shown that five of our heuristics surpassed the current state-of-the-art Modularity Density Maximization heuristic solvers for large graphs. A third contribution is the proposal of six column generation methods. These methods use exact and heuristic auxiliary solvers and an initial variable generator. Comparisons among our proposed column generations and state-of-the-art algorithms were also carried out. The results showed that: (i) two of our methods surpassed the state-of-the-art algorithms in terms of time, and (ii) our methods proved the optimal value for larger instances than current approaches can tackle. Our results suggest clear improvements to the state-of-the-art results for the Modularity Density Maximization problem.application/pdfengHeurísticaGrafos : Arvores : Algoritmos : Algebra booleana : Logica de computadores : Modelagem aritmeticaClusteringModularity density maximizationHeuristic searchMultilevel heuristicsLocal searchColumn generationEfficient modularity density heuristics in graph clustering and their applicationsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisUniversidade Federal do Rio Grande do SulInstituto de InformáticaPrograma de Pós-Graduação em ComputaçãoPorto Alegre, BR-RS2017doutoradoinfo:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL001026068.pdf001026068.pdfTexto completo (inglês)application/pdf10395818http://www.lume.ufrgs.br/bitstream/10183/164066/1/001026068.pdf9128e2c12d6d089fca225a2bd9c192c1MD51TEXT001026068.pdf.txt001026068.pdf.txtExtracted Texttext/plain269763http://www.lume.ufrgs.br/bitstream/10183/164066/2/001026068.pdf.txt6386ce23b935488120b56ca34e03af10MD52THUMBNAIL001026068.pdf.jpg001026068.pdf.jpgGenerated Thumbnailimage/jpeg1023http://www.lume.ufrgs.br/bitstream/10183/164066/3/001026068.pdf.jpg5f4cfa38b11d436364df3ff382e4b504MD5310183/1640662021-05-26 04:43:21.613279oai:www.lume.ufrgs.br:10183/164066Biblioteca Digital de Teses e Dissertaçõeshttps://lume.ufrgs.br/handle/10183/2PUBhttps://lume.ufrgs.br/oai/requestlume@ufrgs.br||lume@ufrgs.bropendoar:18532021-05-26T07:43:21Biblioteca Digital de Teses e Dissertações da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Efficient modularity density heuristics in graph clustering and their applications |
title |
Efficient modularity density heuristics in graph clustering and their applications |
spellingShingle |
Efficient modularity density heuristics in graph clustering and their applications Santiago, Rafael de Heurística Grafos : Arvores : Algoritmos : Algebra booleana : Logica de computadores : Modelagem aritmetica Clustering Modularity density maximization Heuristic search Multilevel heuristics Local search Column generation |
title_short |
Efficient modularity density heuristics in graph clustering and their applications |
title_full |
Efficient modularity density heuristics in graph clustering and their applications |
title_fullStr |
Efficient modularity density heuristics in graph clustering and their applications |
title_full_unstemmed |
Efficient modularity density heuristics in graph clustering and their applications |
title_sort |
Efficient modularity density heuristics in graph clustering and their applications |
author |
Santiago, Rafael de |
author_facet |
Santiago, Rafael de |
author_role |
author |
dc.contributor.author.fl_str_mv |
Santiago, Rafael de |
dc.contributor.advisor1.fl_str_mv |
Lamb, Luis da Cunha |
contributor_str_mv |
Lamb, Luis da Cunha |
dc.subject.por.fl_str_mv |
Heurística Grafos : Arvores : Algoritmos : Algebra booleana : Logica de computadores : Modelagem aritmetica |
topic |
Heurística Grafos : Arvores : Algoritmos : Algebra booleana : Logica de computadores : Modelagem aritmetica Clustering Modularity density maximization Heuristic search Multilevel heuristics Local search Column generation |
dc.subject.eng.fl_str_mv |
Clustering Modularity density maximization Heuristic search Multilevel heuristics Local search Column generation |
description |
Modularity Density Maximization is a graph clustering problem which avoids the resolution limit degeneracy of the Modularity Maximization problem. This thesis aims at solving larger instances than current Modularity Density heuristics do, and show how close the obtained solutions are to the expected clustering. Three main contributions arise from this objective. The first one is about the theoretical contributions about properties of Modularity Density based prioritizers. The second one is the development of eight Modularity Density Maximization heuristics. Our heuristics are compared with optimal results from the literature, and with GAOD, iMeme-Net, HAIN, BMD- heuristics. Our results are also compared with CNM and Louvain which are heuristics for Modularity Maximization that solve instances with thousands of nodes. The tests were carried out by using graphs from the “Stanford Large Network Dataset Collection”. The experiments have shown that our eight heuristics found solutions for graphs with hundreds of thousands of nodes. Our results have also shown that five of our heuristics surpassed the current state-of-the-art Modularity Density Maximization heuristic solvers for large graphs. A third contribution is the proposal of six column generation methods. These methods use exact and heuristic auxiliary solvers and an initial variable generator. Comparisons among our proposed column generations and state-of-the-art algorithms were also carried out. The results showed that: (i) two of our methods surpassed the state-of-the-art algorithms in terms of time, and (ii) our methods proved the optimal value for larger instances than current approaches can tackle. Our results suggest clear improvements to the state-of-the-art results for the Modularity Density Maximization problem. |
publishDate |
2017 |
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2017 |
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