{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":18,"n_cols":5,"rows_kept":18,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[11,2,11,3],[12,2,12,3],[16,2,16,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9910811759503321"]
[null,null,"Titolo",null,"Graph partitioning and graph clustering : 10th DIMACS Implementation Challenge Workshop, February 13-14, 2012, Georgia Institute of Technology, Atlanta, GA / / David A. Bader [and three others], editors"]
[null,null,"Pubbl/distr/stampa",null,"Providence, Rhode Island : , : American Mathematical Society, , 2013"]
[{"$nulls":4},"©2013"]
[null,null,"ISBN",null,"0-8218-9869-8"]
[null,null,"Descrizione fisica",null,"1 online resource (258 p.)"]
[null,null,"Collana",null,"Contemporary mathematics, , 1098-3627 ; ; 588 , 0271-4132"]
[null,null,"Classificazione",null,"05C8568W0505C8268W1068R1005C5005C65"]
[null,null,"Disciplina",null,"511/.5"]
[null,null,"Soggetti",null,"Graph algorithms"]
[{"$nulls":4},"Graph theory"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"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 --  "]
[{"$nulls":4},"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 --  Using 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 -- Complete hierarchical cut-clustering: A case study on expansion and modularity -- A partitioning-based divisive clustering technique for maximizing the modularity -- An ensemble learning strategy for graph clustering -- Parallel community detection for massive graphs -- Graph coarsening and clustering on the GPU."]