Seed-Growth Heuristics for Graph Bisection

Abstract

We investigate a family of algorithms for graph bisection that are based on a simple local connectivity heuristic, which we call seed-growth. We show how the heuristic can be combined with stochastic search procedures and a postprocess application of the Kernighan-Lin algorithm. In a series of time-equated comparisons against large-sample runs of pure Kernighan-Lin, the new algorithms find bisections of the same or superior quality. Their performance is particularly good on structured graphs representing important industrial applications. An appendix provides further favorable comparisons to other published results. Our experimental methodology and extensive empirical results provide a solid foundation for further empirical investigation of graph-bisection algorithms.