Memetic Graph Clustering

Abstract

It is common knowledge that there is no single best strategy for graph clustering, which justifies a plethora of existing approaches. In this paper, we present a general memetic algorithm, VieClus, to tackle the graph clustering problem. This algorithm can be adapted to optimize different objective functions. A key component of our contribution are natural recombine operators that employ ensemble clusterings as well as multi-level techniques. Lastly, we combine these techniques with a scalable communication protocol, producing a system that is able to compute high-quality solutions in a short amount of time. We instantiate our scheme with local search for modularity and show that our algorithm successfully improves or reproduces all entries of the 10th DIMACS implementation challenge under consideration using a small amount of time.

Recent Progress on Graph Partitioning Problems Using Evolutionary Computation

“…To develop this algorithm, effective multi-level recombination and mutation processes were developed. Biedermann et al [2] proposed a general memetic algorithm to solve a graph clustering problem. In this algorithm, natural recombination operators using ensemble clustering and multi-level techniques were adopted.…”