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001			EBC3113242
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006			m o d |
007			cr cnu||||||||
008			240724s2013 xx o ||||0 eng d
020			_a9780821898697 _q(electronic bk.)
020			_z9780821890387
035			_a(MiAaPQ)EBC3113242
035			_a(Au-PeEL)EBL3113242
035			_a(CaPaEBR)ebr10878697
035			_a(OCoLC)840599907
040			_aMiAaPQ _beng _erda _epn _cMiAaPQ _dMiAaPQ
050		4	_aQA166.245 -- .D56 2013eb
082	0		_a511/.5
100	1		_aBader, David A.
245	1	0	_aGraph Partitioning and Graph Clustering.
250			_a1st ed.
264		1	_aProvidence : _bAmerican Mathematical Society, _c2013.
264		4	_c©2013.
300			_a1 online resource (258 pages)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aContemporary Mathematics ; _vv.588
505	0		_aIntro -- Preface -- 1. Introducing the 10th Challenge - Graph Partitioning and Graph Clustering -- 2. Key Results -- 3. Challenge Description -- 4. Contributions to this Collection -- 5. Directions for Further Research -- High quality graph partitioning -- 1. Introduction -- 2. Preliminaries -- 3. Related Work -- 4. Karlsruhe Fast Flow Partitioner -- 5. KaFFPa Evolutionary -- 6. Experiments -- 7. Conclusion and Future Work -- References -- Abusing a hypergraph partitioner for unweighted graph partitioning -- 1. Introduction -- 2. Mondriaan -- 3. Results -- 4. Conclusion -- References -- Parallel partitioning with Zoltan: Is hypergraph partitioning worth it? -- 1. Introduction -- 2. Models and Metrics -- 3. Overview of the Zoltan Hypergraph Partitioner -- 4. Experiments -- 5. Conclusions -- Acknowledgements -- References -- UMPa: A multi-objective, multi-level partitioner for communication minimization -- 1. Introduction -- 2. Background -- 3. UMPa: A multi-objective partitioning tool for communication minimization -- 4. Experimental results -- 5. Conclusions and future work -- References -- Appendix A. DIMACS Challenge Results -- Shape optimizing load balancing for MPI-parallel adaptive numerical simulations -- 1. Introduction -- 2. Related Work -- 3. Diffusion-based Repartitioning with DibaP -- 4. PDibaP: Parallel DibaP for Repartitioning -- 5. Experiments -- 6. Conclusions -- References -- Graph partitioning for scalable distributed graph computations -- 1. Introduction -- 2. Parallel Breadth-first Search -- 3. Analysis of Communication Costs -- 4. Graph and Hypergraph Partitioning Metrics -- 5. Experimental Setup -- 6. Microbenchmarking Collectives Performance -- 7. Performance Analysis and Results -- 8. Conclusions and Future Work -- Acknowledgments -- References -- Appendix on edge count per processor.
505	8		_aUsing graph partitioning for efficient network modularity optimization -- 1. Introduction -- 2. Reduction of modularity optimization to minimum weighted cut -- 3. Implementation of the modularity optimization algorithm based on the Metis package -- 4. Comparison on DIMACS testbed graphs -- 5. Conclusion -- References -- Modularity maximization in networks by variable neighborhood search -- 1. Introduction -- 2. Description of the heuristic -- 3. Description of the exact method -- 4. Experimental Results -- 5. Conclusion -- References -- Network clustering via clique relaxations: A community based approach -- 1. Introduction -- 2. Background -- 3. Clustering Algorithm -- 4. Computational Results -- 5. Conclusion -- Acknowledgements -- References -- Identifying base clusters and their application to maximizing modularity -- 1. Introduction -- 2. Background -- 3. Finding Base Clusters in Complex Networks -- 4. Modularity Maximization Using Base Clusters -- 5. Discussion and Future Work -- References -- Complete hierarchical cut-clustering: A case study on expansion and modularity -- 1. Introduction -- 2. Preliminaries -- 3. Experimental Study -- 4. Conclusion -- References -- A partitioning-based divisive clustering technique for maximizing the modularity -- 1. Introduction -- 2. Background -- 3. Algorithms -- 4. Experiments -- 5. Conclusion -- Acknowledgment -- References -- Appendix A. DIMACS Challenge results -- An ensemble learning strategy for graph clustering -- 1. Introduction -- 2. Ensemble Learning -- 3. Core Groups Graph Clustering Scheme -- 4. Modularity and its Optimization -- 5. Evaluation -- 6. A Global Analysis View on the CGGC Scheme -- 7. Conclusion -- References -- Parallel community detection for massive graphs -- 1. Communities in Graphs -- 2. Parallel Agglomerative Community Detection.
505	8		_a3. Mapping the Agglomerative Algorithm to Threaded Platforms -- 4. Parallel Performance -- 5. Related Work -- 6. Observations -- Acknowledgments -- References -- Graph coarsening and clustering on the GPU -- 1. Introduction -- 2. Clustering -- 3. Coarsening -- 4. Parallel implementation -- 5. Results -- 6. Conclusion -- 7. Appendix -- Acknowledgements -- References.
588			_aDescription based on publisher supplied metadata and other sources.
590			_aElectronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
650		0	_aGraph algorithms -- Congresses.
650		0	_aGraph theory -- Congresses.
655		4	_aElectronic books.
700	1		_aMeyerhenke, Henning.
700	1		_aSanders, Peter.
700	1		_aWagner, Dorothea.
776	0	8	_iPrint version: _aBader, David A. _tGraph Partitioning and Graph Clustering _dProvidence : American Mathematical Society,c2013 _z9780821890387
797	2		_aProQuest (Firm)
830		0	_aContemporary Mathematics
856	4	0	_uhttps://ebookcentral.proquest.com/lib/orpp/detail.action?docID=3113242 _zClick to View
999			_c68776 _d68776