Separators for sphere-packings and nearest neighbor graphs
Journal of the ACM (JACM)
Min-Max-Boundary Domain Decomposition
COCOON '98 Proceedings of the 4th Annual International Conference on Computing and Combinatorics
Partitioning Planar Graphs with Costs and Weights
ALENEX '02 Revised Papers from the 4th International Workshop on Algorithm Engineering and Experiments
A Parallel Multilevel Metaheuristic for Graph Partitioning
Journal of Heuristics
Partitioning planar graphs with costs and weights
Journal of Experimental Algorithmics (JEA)
Bipartite isoperimetric graph partitioning for data co-clustering
Data Mining and Knowledge Discovery
Approximate center points with proofs
Proceedings of the twenty-fifth annual symposium on Computational geometry
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
A Matrix Partitioning Interface to PaToH in MATLAB
Parallel Computing
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
Approximate centerpoints with proofs
Computational Geometry: Theory and Applications
Sparse matrices in Matlab*P: design and implementation
HiPC'04 Proceedings of the 11th international conference on High Performance Computing
Towards effective partition management for large graphs
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Line orthogonality in adjacency eigenspace with application to community partition
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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We investigate a method of dividing an irregular mesh into equal-sized pieces with few interconnecting edges. The method's novel feature is that it exploits the geometric coordinates of the mesh vertices. It is based on theoretical work of Miller, Teng, Thurston, and Vavasis, who showed that certain classes of "well-shaped" finite-element meshes have good separators. The geometric method is quite simple to implement: we describe a \sc Matlab code for it in some detail. The method is also quite efficient and effective: we compare it with some other methods, including spectral bisection.