Multilevel Graph Clustering with Density-Based Quality Measures

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BIBLIOGRAPHY[14] B.L. Chamberlain. Graph partitioning algorithms for distributing workloads ofparallel computations. University of Washington Technical Report UW-CSE-98-10, 3, 1998.[15] Aaron Clauset, M. E. J. Newman, and Cristopher Moore. Finding communitystructure in very large networks. Physical Review E, 70:066111, December 2004.[16] Gábor Csárdi and Tamás Nepusz. The igraph software package for complexnetwork research. InterJournal Complex Systems, 1695, 2006.[17] L. Danon, A. Díaz-Guilera, and A. Arenas. Effect of size heterogeneity oncommunity identification in complex networks. Arxiv preprint physics/0601144,2006.[18] Leon Danon, Albert Díaz-Guilera, Jordi Duch, and Alex Arenas. Comparingcommunity structure identification. Journal of Statistical Mechanics: Theoryand Experiment, 2005:P09008, 2005.[19] Hristo Djidjev. A scalable multilevel algorithm for graph clustering and communitystructure detection. Workshop on Algorithms and Models for the WebGraph, Lecture Notes in Computer Science, pages LA–UR–06–6261, 2006.[20] L. Donetti and M. A Munoz. Improved spectral algorithm for the detection ofnetwork communities. physics/0504059, April 2005.[21] Luca Donetti and Miguel A. Muñoz. Detecting network communities: a newsystematic and efficient algorithm. Journal of Statistical Mechanics: Theoryand Experiment, 2004:P10012, 2004.[22] Jonathan Doye and David Wales. On the thermodynamics of global optimization.cond-mat/9709019, September 1997. Phys. Rev. Lett. 80, 1357 (1998).[23] Jordi Duch and Alex Arenas. Community detection in complex networks usingextremal optimization. Physical Review E (Statistical, Nonlinear, and SoftMatter Physics), 72:027104–4, 2005.[24] C.M. Fiduccia and R.M. Mattheyses. A linear-time heuristic for improvingnetwork partitions. In Design Automation, 1982. 19th Conference on, pages175–181, 1982.[25] Santo Fortunato and Marc Barthelemy. Resolution limit in community detection.Proceedings of the National Academy of Sciences, 104(1):36, 2007.[26] Santo Fortunato, Vito Latora, and Massimo Marchiori. Method to find communitystructures based on information centrality. Physical Review E (Statistical,Nonlinear, and Soft Matter Physics), 70:056104–13, November 2004.[27] H. N. Gabow, Z. Galil, and T.H. Spencer. Efficient Implementation of GraphAlgorithms Using Contraction. Journal of the Association for Computing Machinery,36(3):540–572, 1989.94
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Brandenburgische Technische Univers
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ContentsList of FiguresList of Tabl
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List of Figures1.1 Graph of the Mex
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1 IntroductionSince the rise of com
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1.2 Objectives and Outline1.2 Objec
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2 Graph ClusteringThis chapter intr
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2.2 The Modularity Measure of Newma
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2.3 Density-Based Clustering Qualit
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2.4 Fundamental Clustering Strategi
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3 The Multi-Level Refinement Algori
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3.1 The Multi-Level Schemeas starti
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3.2 Graph CoarseningData: graph,sel
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3.2 Graph Coarseningnearly no edges
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3.3 Merge SelectorsExtent Name Desc
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3.3 Merge Selectorsdifferent size.
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3.3 Merge SelectorsThe probability
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3.3 Merge SelectorsAs selection qua
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3.3 Merge Selectorsvectors the eige
Page 53 and 54: 3.4 Cluster Refinementleave the loc
Page 55 and 56: 3.4 Cluster Refinementmoving v from
Page 57 and 58: 3.4 Cluster RefinementAlgorithm Sea
Page 59 and 60: 3.4 Cluster RefinementData: graph,c
Page 61 and 62: 3.4 Cluster RefinementModularity0.2
Page 63 and 64: 3.5 Further Implementation NotesInd
Page 65 and 66: 3.5 Further Implementation NotesBOO
Page 67: 3.5 Further Implementation Notesfor
Page 70 and 71: 4 Evaluationparameter component des
Page 72 and 73: 4 Evaluationsignificance scale also
Page 74 and 75: 4 EvaluationModularity by Match Fra
Page 76 and 77: 4 Evaluation5% 10% 30% 50% 100%G-no
Page 78 and 79: 4 Evaluationmean modularity0.50 0.5
Page 80 and 81: 4 Evaluation1 2 3 4RWreach-none 1 0
Page 82 and 83: 4 EvaluationG-none M-none G-sgrd M-
Page 84 and 85: 4 Evaluationmean modularity time DI
Page 86 and 87: 4 EvaluationRuntime vs. Graph SizeR
Page 88 and 89: 4 EvaluationComparison of Modularit
Page 90 and 91: 4 Evaluation(a) karate(b) dolphinsF
Page 92 and 93: 4 Evaluation(a) jazz(b) celegans me
Page 94 and 95: 4 Evaluationadministrators, and gra
Page 97 and 98: 5 Results and Future WorkThe object
Page 99 and 100: 5.3 Directions for Future Workstrat
Page 101: 5.3 Directions for Future Workties
Page 106 and 107: BIBLIOGRAPHY[42] H. Jeong, B. Tombo
Page 108 and 109: BIBLIOGRAPHY[71] A. J. Soper and C.
Page 110 and 111: A The Benchmark Graph Collectionsub
Page 112 and 113: B Clustering ResultsRWreach-sgrd 1
Page 114: B Clustering Resultswalktrap leadin
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Multilevel Graph Clustering with Density-Based Quality Measures

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