The complexity of finding optimal subgraphs to represent spatial correlation

Enright, J. , Lee, D. , Meeks, K. , Pettersson, W. and Sylvester, J. (2021) The complexity of finding optimal subgraphs to represent spatial correlation. In: Du, D.-Z., Du, D., Wu, C. and Xu, D. (eds.) Combinatorial Optimization and Applications. Series: Lecture Notes in Computer Science (13135). Springer, pp. 152-166. ISBN 9783030926809 (doi: 10.1007/978-3-030-92681-6_13)

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Abstract

Understanding spatial correlation is vital in many fields including epidemiology and social science. Lee, Meeks and Pettersson (Stat. Comput. 2021) recently demonstrated that improved inference for areal unit count data can be achieved by carrying out modifications to a graph representing spatial correlations; specifically, they delete edges of the planar graph derived from border-sharing between geographic regions in order to maximise a specific objective function. In this paper we address the computational complexity of the associated graph optimisation problem. We demonstrate that this problem cannot be solved in polynomial time unless P = NP; we further show intractability for two simpler variants of the problem. We follow these results with two parameterised algorithms that exactly solve the problem in polynomial time in restricted settings. The first of these utilises dynamic programming on a tree decomposition, and runs in polynomial time if both the treewidth and maximum degree are bounded. The second algorithm is restricted to problem instances with maximum degree three, as may arise from triangulations of planar surfaces, but is an FPT algorithm when the maximum number of edges that can be removed is taken as the parameter.

Item Type:Book Sections
Additional Information:Proceedings of the 15th International Conference, COCOA 2021, Tianjin, China, December 17–19, 2021. All authors gratefully acknowledge funding from the Engineering and Physical Sciences Research Council (ESPRC) grant number EP/T004878/1 for this work, while Meeks was also supported by a Royal Society of Edinburgh Personal Research Fellowship (funded by the Scottish Government).
Status:Published
Glasgow Author(s) Enlighten ID:Lee, Professor Duncan and Pettersson, Dr William and Enright, Dr Jessica and Meeks, Dr Kitty and Sylvester, Dr John
Authors: Enright, J., Lee, D., Meeks, K., Pettersson, W., and Sylvester, J.
College/School:College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Publisher:Springer
ISSN:0302-9743
ISBN:9783030926809
Published Online:11 December 2021

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
305944Multilayer Algorithmics to Leverage Graph StructureKitty MeeksEngineering and Physical Sciences Research Council (EPSRC)EP/T004878/1M&S - Statistics