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.
Cite
@article{arxiv.1802.07034,
title = {Memetic Graph Clustering},
author = {Sonja Biedermann and Monika Henzinger and Christian Schulz and Bernhard Schuster},
journal= {arXiv preprint arXiv:1802.07034},
year = {2018}
}