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Memetic graph clustering. (English) Zbl 07286676
D’Angelo, Gianlorenzo (ed.), 17th symposium on experimental algorithms, SEA 2018, June 27–29, 2018, L’Aquila, Italy. Wadern: Schloss Dagstuhl – Leibniz Zentrum für Informatik (ISBN 978-3-95977-070-5). LIPIcs – Leibniz International Proceedings in Informatics 103, Article 3, 15 p. (2018).
Summary: 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.
For the entire collection see [Zbl 1390.68017].

MSC:
68Wxx Algorithms in computer science
Software:
KaFFPa; KaHIP; LPAm+; VNDS
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