Universität zu Köln

Lee, Deokjae, Hwang, S., Choi, S. and Kahng, B. (2018). Decremental dynamic algorithm to trace mutually connected clusters. SoftwareX, 7. S. 273 - 281. AMSTERDAM: ELSEVIER SCIENCE BV. ISSN 2352-7110

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Abstract

The structure and dynamics of interdependent networks model catastrophic failures in complex systems that are interdependent. Percolation transitions on these networks exhibit hybrid phase transitions, which have significant practical implications for the early detection of large-scale failures. While the computer simulation of the percolation transitions and related dynamics can effectively be reduced to the computation of mutually connected clusters, such a computation is nontrivial, and several algorithms to handle the task have been proposed. Here we introduce a C++ implementation of one of the algorithms. This implementation uses intrusive data structures and thus provides a greater flexibility for applications in which efficient memory access is critical. The data structures, which we provide as a part of the library, are also useful for general percolation problems. (c) 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Lee, Deokjae UNSPECIFIED UNSPECIFIED UNSPECIFIED Hwang, S. UNSPECIFIED UNSPECIFIED UNSPECIFIED Choi, S. UNSPECIFIED UNSPECIFIED UNSPECIFIED Kahng, B. UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-199668
DOI:	10.1016/j.softx.2018.08.002
Journal or Publication Title:	SoftwareX
Volume:	7
Page Range:	S. 273 - 281
Date:	2018
Publisher:	ELSEVIER SCIENCE BV
Place of Publication:	AMSTERDAM
ISSN:	2352-7110
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language Computer Science, Software Engineering Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/19966