Graph partitioning and graph clustering : 10th DIMACS Implementation Challenge Workshop, February 1314, 2012, Georgia Institute of Technology, Atlanta, GA
 Responsibility
 David A. Bader, Henning Meyerhenke, Peter Sanders, Dorothea Wagner, editors.
 Language
 English.
 Publication
 Providence, Rhode Island : American Mathematical Society, [2013]
 Copyright notice
 ©2013
 Physical description
 xiii, 240 pages : illustarions ; 26 cm.
 Series
 Contemporary mathematics (American Mathematical Society) v. 588.
Access
Creators/Contributors
 Meeting
 DIMACS Implementation Challenge Workshop (10th : 2012 : Atlanta, Ga.)
 Contributor
 Bader, David A., 1969 editor of compilation.
 Meyerhenke, Henning, 1978 editor of compilation.
 Sanders, Peter, editor of compilation.
 Wagner, Dorothea, 1957 editor of compilation.
Contents/Summary
 Bibliography
 Includes bibliographical references.
 Contents

 Table of Contents * Preface  by David A. Bader, Henning Meyerhenke, Peter Sanders, and Dorothea Wagner * High quality graph partitioning  by P. Sanders and C. Schulz * Abusing a Hypergraph Partitioner for Unweighted Graph Partitioning  by B. O. Fagginger Auer and R. H. Bisseling * Parallel partitioning with Zoltan: Is hypergraph partitioning worth it?  by S. Rajamanickam and E. G. Boman * UMPa: A multiobjective, multilevel partitioner for communication minimization  by U. V. Catalyurek, M. Deveci, K. Kaya, and K. Ucar * Shape optimizing load balancing for MPIparallel adaptive numerical simulations  by H. Meyerhenke * Graph partitioning for scalable distributed graph computations  by A. Buluc and K. Madduri * Using graph partitioning for efficient network modularity optimization  by H. Djidjev and M. Onus * Modularity maximization in networks by variable neighborhood search  by D. Aloise, G. Caporossi, P. Hansen, L. Liberti, S. Perron, and M. Ruiz * Network clustering via clique relaxations: A community based approach  by A. Verma and S. Butenko * Identifying base clusters and their application to maximizing modularity  by S. Srinivasan, T. Chakraborty, and S. Bhowmick * Complete hierarchical cutclustering: A case study on expansion and modularity  by M. Hamann, T. Hartmann, and D. Wagner * A partitioningbased divisive clustering technique for maximizing the modularity  by U. V. Catalyurek, K. Kaya, J. Langguth, and B. Ucar * An ensemble learning strategy for graph clustering  by M. Ovelgonne and A. GeyerSchulz * Parallel community detection for massive graphs  by E. J. Riedy, H. Meyerhenke, D. Ediger, and D. A. Bader * Graph coarsening and clustering on the GPU  by B. O. Fagginger Auer and R. H. Bisseling.
 (source: Nielsen Book Data)
 Publisher's Summary
 Graph partitioning and graph clustering are ubiquitous subtasks in many applications where graphs play an important role. Generally speaking, both techniques aim at the identification of vertex subsets with many internal and few external edges. To name only a few, problems addressed by graph partitioning and graph clustering algorithms are: li>What are the communities within an (online) social network? * How do I speed up a numerical simulation by mapping it efficiently onto a parallel computer? * How must components be organised on a computer chip such that they can communicate efficiently with each other? * What are the segments of a digital image? * Which functions are certain genes (most likely) responsible for? * The 10th DIMACS Implementation Challenge Workshop was devoted to determining realistic performance of algorithms where worst case analysis is overly pessimistic and probabilistic models are too unrealistic. Articles in the volume describe and analyse various experimental data with the goal of getting insight into realistic algorithm performance in situations where analysis fails. This book is published in cooperation with the Center for Discrete Mathematics and Theoretical Computer Science.
(source: Nielsen Book Data)
Subjects
 Subject
 Graph algorithms > Congresses.
 Graph theory > Congresses.
Bibliographic information
 Publication date
 2013
 Series
 Contemporary mathematics ; volume 588
 ISBN
 9780821890387 (alk. paper)
 0821890387 (alk. paper)