A new approach to the minimum cut problem
Journal of the ACM (JACM)
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
A linear-time heuristic for improving network partitions
DAC '82 Proceedings of the 19th Design Automation Conference
A Combined Evolutionary Search and Multilevel Optimisation Approach to Graph-Partitioning
Journal of Global Optimization
A simpler linear time 2/3 - ε approximation for maximum weight matching
Information Processing Letters
Engineering Route Planning Algorithms
Algorithmics of Large and Complex Networks
A new diffusion-based multilevel algorithm for computing graph partitions
Journal of Parallel and Distributed Computing
Linear time 1/2 -approximation algorithm for maximum weighted matching in general graphs
STACS'99 Proceedings of the 16th annual conference on Theoretical aspects of computer science
Engineering algorithms for approximate weighted matching
WEA'07 Proceedings of the 6th international conference on Experimental algorithms
Contraction hierarchies: faster and simpler hierarchical routing in road networks
WEA'08 Proceedings of the 7th international conference on Experimental algorithms
The university of Florida sparse matrix collection
ACM Transactions on Mathematical Software (TOMS)
Engineering multilevel graph partitioning algorithms
ESA'11 Proceedings of the 19th European conference on Algorithms
Engineering graph partitioning algorithms
SEA'12 Proceedings of the 11th international conference on Experimental Algorithms
| Hi-index | 0.00 |
We present a multi-level graph partitioning algorithm based on the extreme idea to contract only a single edge on each level of the hierarchy. This obviates the need for a matching algorithm and promises very good partitioning quality since there are very few changes between two levels. Using an efficient data structure and new flexible ways to break local search improvements early, we obtain an algorithm that scales to large inputs and produces the best known partitioning results for many inputs. For example, in Walshaw's well known benchmark tables we achieve 155 improvements dominating the entries for large graphs.