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Analysis of an iterated greedy heuristic for vertex clique covering (2018)
Journal Article
Chalupa, D., & Pospíchal, J. (2018). Analysis of an iterated greedy heuristic for vertex clique covering. Computing and Informatics, 37(2), 385-404. doi:10.4149/cai_2018_2_385

The aim of the vertex clique covering problem (CCP) is to cover the vertices of a graph with as few cliques as possible. We analyse the iterated greedy (IG) algorithm for CCP, which was previously shown to provide strong empirical results for real-wo... Read More

Computational methods for finding long simple cycles in complex networks (2017)
Journal Article
Chalupa, D., Balaghan, P., Hawick, K. A., & Gordon, N. A. (2017). Computational methods for finding long simple cycles in complex networks. Knowledge-Based Systems, 125, 96-107. doi:10.1016/j.knosys.2017.03.022

© 2017 Elsevier B.V. Detection of long simple cycles in real-world complex networks finds many applications in layout algorithms, information flow modelling, as well as in bioinformatics. In this paper, we propose two computational methods for findin... Read More

Construction of near-optimal vertex clique covering for real-world networks (2015)
Journal Article
Chalupa, D. (2015). Construction of near-optimal vertex clique covering for real-world networks. Computing and Informatics, 34(6), 1397-1417

We propose a method based on combining a constructive and a bounding heuristic to solve the vertex clique covering problem (CCP), where the aim is to partition the vertices of a graph into the smallest number of classes, which induce cliques. Searchi... Read More

Partitioning networks into cliques : a randomized heuristic approach (2014)
Journal Article
Chalupa, D. (2014). Partitioning networks into cliques : a randomized heuristic approach. Information Sciences and Technologies Bulletin of the ACM Slovakia, 6(3), 1-8

In the context of community detection in social networks, the term community can be grounded in the strict way that simply everybody should know each other within the community. We consider the corresponding community detection problem. We search for... Read More