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.

Table of 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 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