Graph partitioning and graph clustering : 10th DIMACS Implementation Challenge Workshop, February 13-14, 2012, Georgia Institute of Technology, Atlanta, GA
著者
書誌事項
Graph partitioning and graph clustering : 10th DIMACS Implementation Challenge Workshop, February 13-14, 2012, Georgia Institute of Technology, Atlanta, GA
（Contemporary mathematics, 588）
American Mathematical Society, c2013
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注記
"Center for Discrete Mathematics and Theoretical Computer Science"
Includes bibliographical references
内容説明・目次
内容説明
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.
目次
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 multi-objective, multi-level partitioner for communication minimization - by U. V. Catalyurek, M. Deveci, K. Kaya, and K. Ucar
Shape optimizing load balancing for MPI-parallel 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 cut-clustering: A case study on expansion and modularity - by M. Hamann, T. Hartmann, and D. Wagner
A partitioning-based 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. Geyer-Schulz
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
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