Skip to main content

Computational methods for finding long simple cycles in complex networks

Chalupa, David; Balaghan, Phininder; Hawick, Ken A.; Gordon, Neil A.

Authors

David Chalupa

Phininder Balaghan

Ken A. Hawick



Abstract

© 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 finding long cycles in real-world networks. The first method is an exact approach based on our own integer linear programming formulation of the problem and a data mining pipeline. This pipeline ensures that the problem is solved as a sequence of integer linear programs. The second method is a multi-start local search heuristic, which combines an initial construction of a long cycle using depth-first search with four different perturbation operators. Our experimental results are presented for social network samples, graphs studied in the network science field, graphs from DIMACS series, and protein-protein interaction networks. These results show that our formulation leads to a significantly more efficient exact approach to solve the problem than a previous formulation. For 14 out of 22 networks, we have found the optimal solutions. The potential of heuristics in this problem is also demonstrated, especially in the context of large-scale problem instances.

Publication Date 2017-06
Journal Knowledge-based systems
Print ISSN 0950-7051
Electronic ISSN 1872-7409
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 125
Pages 96-107
APA6 Citation 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 . https://doi.org/10.1016/j.knosys.2017.03.022
DOI https://doi.org/10.1016/j.knosys.2017.03.022
Keywords Long simple cycles; Long cycles; Complex networks; Integer linear programming; Graph algorithms; Local search; Hamiltonian cycles
Publisher URL http://www.sciencedirect.com/science/article/pii/S0950705117301491
Copyright Statement ©2018, Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Additional Information This is the accepted manuscript of an article published in Knowledge-based systems, 2017. The version of record is available at the DOI link in this record

Files

Article (2.1 Mb)
PDF

Copyright Statement
©2018, Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/





You might also like



Downloadable Citations

;