Memetic Graph Clustering

TL;DR

The paper introduces VieClus, a versatile memetic algorithm for graph clustering that employs ensemble and multi-level techniques, achieving high-quality solutions efficiently across various objectives.

Contribution

It presents a flexible memetic algorithm with novel recombine operators and scalable communication, capable of optimizing different graph clustering objectives.

Findings

Successfully improves or reproduces all entries in the DIMACS challenge

Operates efficiently with a small amount of computational time

Adapts to optimize different objective functions

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.