A k-way Graph Partitioning Algorithm Based on Clustering by Eigenvector

Abstract

AbstractThe recursive spectral bisection for the k-way graph partition has been underestimated because it tries to balance the bipartition strictly. However, by loosening the balancing constraint, the spectral bisection can identify clusters efficiently. We propose a k-way graph partitioning algorithm based on clustering using recursive spectral bisection. After a graph is divided into a partition, the partition is adjusted in order to meet the balancing constraint. Experimental results show that the clustering based k-way partitioning generates partitions with 83.8~ 108.4% cutsets compared to the strict recursive spectral bisections or multi-level partitions.