Graph Partitioning and Graph Clustering
About this Title
David A. Bader, Georgia Institute of Technology, Atlanta, GA, Henning Meyerhenke, Karlsruhe Institute of Technology, Karlsruhe, Germany, Peter Sanders, Karlsruhe Institute of Technology, Karlsruhe, Germany and Dorothea Wagner, Karlsruhe Institute of Technology, Karlsruhe, Germany, Editors
Publication: Contemporary Mathematics
Publication Year 2013: Volume 588
ISBNs: 978-0-8218-9038-7 (print); 978-0-8218-9869-7 (online)
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.
Graduate students and research mathematicians interested in graph theory and combinatorial algorithms.
Table of Contents
- Peter Sanders and Christian Schulz – High quality graph partitioning
- B. O. Fagginger Auer and R. H. Bisseling – Abusing a hypergraph partitioner for unweighted graph partitioning
- Sivasankaran Rajamanickam and Erik G. Boman – Parallel partitioning with Zoltan: Is hypergraph partitioning worth it?
- Ümit V. Çatalyürek, Mehmet Deveci, Kamer Kaya and Bora Uçar – UMPa: A multi-objective, multi-level partitioner for communication minimization
- Henning Meyerhenke – Shape optimizing load balancing for MPI-parallel adaptive numerical simulations
- Aydın Buluç and Kamesh Madduri – Graph partitioning for scalable distributed graph computations
- Hristo Djidjev and Melih Onus – Using graph partitioning for efficient network modularity optimization
- Daniel Aloise, Gilles Caporossi, Pierre Hansen, Leo Liberti, Sylvain Perron and Manuel Ruiz – Modularity maximization in networks by variable neighborhood search
- Anurag Verma and Sergiy Butenko – Network clustering via clique relaxations: A community based approach
- Sriram Srinivasan, Tanmoy Chakraborty and Sanjukta Bhowmick – Identifying base clusters and their application to maximizing modularity
- Michael Hamann, Tanja Hartmann and Dorothea Wagner – Complete hierarchical cut-clustering: A case study on expansion and modularity
- Ümit V. Çatalyürek, Kamer Kaya, Johannes Langguth and Bora Uçar – A partitioning-based divisive clustering technique for maximizing the modularity
- Michael Ovelgönne and Andreas Geyer-Schulz – An ensemble learning strategy for graph clustering
- E. Jason Riedy, Henning Meyerhenke, David Ediger and David A. Bader – Parallel community detection for massive graphs
- B. O. Fagginger Auer and R. H. Bisseling – Graph coarsening and clustering on the GPU