Multi-level spectral hypergraph partitioning with arbitrary vertex sizes

This paper presents a new spectral partitioning formation which directly incorporates vertex size information. The new formulation results in a generalized eigenvalue problem, and this problem is reduced to the standard eigenvalue problem. Experimental results show that incorporating vertex sizes into the eigenvalue calculation produces results that are 50% better than the standard formation in terms of scaled ratio-cut cost, even when a Kernighan-Lin style iterative improvement algorithm taking into account vertex sizes is applied as a post-processing step. To evaluate the new method for use in multi-level partitioning, we combine the partitioner with a multi-level bottom-up clustering algorithm and an iterative improvement algorithm for partition refinement. Experimental results show that our new spectral algorithm is more effective than the standard spectral formulation and other partitioners in the multi-level partitioning of hypergraphs.