Graph Partitioning and Graph Clustering

David A. Bader, Georgia Institute of Technology, Atlanta, GA, USA. Henning Meyerhenke, Karlsruhe Institute of Technology, Germany.Peter Sanders, Karlsruhe Institute of Technology, Germany. Dorothea Wagner, Karlsruhe Institute of Technology, Germany.

• Table of Contents Preface - by David A. Bader, Henning Meyerhenke, Peter Sanders, and Dorothea WagnerHigh quality graph partitioning - by P. Sanders and C. SchulzAbusing a Hypergraph Partitioner for Unweighted Graph Partitioning - by B. O. Fagginger Auer and R. H. BisselingParallel partitioning with Zoltan: Is hypergraph partitioning worth it? - by S. Rajamanickam and E. G. BomanUMPa: A multi-objective, multi-level partitioner for communication minimization - by U. V. Catalyurek, M. Deveci, K. Kaya, and K. UcarShape 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