Description 
xiii, 240 pages : illustrations ; 26 cm. 
Content Type 
text 
Format 
volume 
Series 
Contemporary mathematics ; v. 588 

Contemporary mathematics (American Mathematical Society) ; v. 588.

Bibliography 
Includes bibliographical references. 
Contents 
Preface / David A. Bader, Henning Meyerhenke, Peter Sanders, and Dorothea Wagner  High Quality Graph Partitioning / Peter Sanders and Christian Schulz  Abusing a Hypergraph Partitioner for Unweighted Graph Partitioning / B.O. Fagginger Auer and R.H. Bisseling  Parallel Partitioning with Zoltan: Is Hypergraph Partitioning Worth It? / Sivasankaran Rajamanickam and Erik G. Boman  UMPa: A Multiobjective, multilevel partitioner for communication minimization / Ümit V. Çatalyürek, Mehmet Deveci, Kamer Kaya, and Bora Uçar  Shape Optimizing Load Balancing for MPIParallel Adaptive Numerical Simulations / Henning Meyerhenke  Graph Partitioning for Scalable Distributed Graph Computations / Aydin Buluç and Kamesh Madduri  Using Graph Partitioning for Efficient Network Modularity Optimization / Hristo Djidjev and Melih Onus  Modularity Maximization in Networks by Variable Neighborhood Search / Daniel Aloise, Gilles Caporossi, Pierre Hansen, Leo Liberti, Sylvain Perron, and Manuel Ruiz  Network Clustering via Clique Relaxations: A Community Based Approach / Anurag Verma and Sergiy Butenko  Identifying Base Clusters and Their Application to Maximizing Modularity / Sriram Srinivasan, Tanmoy Chakraborty, and Sanjukta Bhowmick  Complete Hierarchical CutClustering: A Case Study on Expansion and Modularity / Michael Hamann, Tanja Hartmann, and Dorothea Wagner  A PartitioningBased Divisive Clustering Technique for Maximizing the Modularity / Ümit V. Çatalyürek, Kamer Kaya, Johannes Langguth, and Bora Uçar  An Ensemble Learning Strategy for Graph Clustering / Michael Ovelgönne and Andreas GeyerSchulz  Parallel Community Detection for Massive Graphs / E. Jason Riedy, Henning Meyerhenke, David Ediger, and David A. Bader  Graph Coarsening and Clustering on the GPU / B.O. Fagginger Auer and R.H. Bisseling. 
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: 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 organized 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 analyze 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."Publisher's website. 
Subject 
Graph algorithms  Congresses.


Graph theory  Congresses.

Related Names 
Bader, David A., 1969 editor of compilation.


Meyerhenke, Henning, 1978 editor of compilation.


Sanders, Peter, editor of compilation.


Wagner, Dorothea, 1957 editor of compilation.

ISBN 
0821890387 (alk. paper) 

9780821890387 (alk. paper) 
OCLC number 
829644505 
