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Portfolios of Subgraph Isomorphism Algorithms

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Learning and Intelligent Optimization (LION 2016)

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Abstract

Subgraph isomorphism is a computationally challenging problem with important practical applications, for example in computer vision, biochemistry, and model checking. There are a number of state-of-the-art algorithms for solving the problem, each of which has its own performance characteristics. As with many other hard problems, the single best choice of algorithm overall is rarely the best algorithm on an instance-by-instance. We develop an algorithm selection approach which leverages novel features to characterise subgraph isomorphism problems and dynamically decides which algorithm to use on a per-instance basis. We demonstrate substantial performance improvements on a large set of hard benchmark problems. In addition, we show how algorithm selection models can be leveraged to gain new insights into what affects the performance of an algorithm.

L. Kotthoff – This work was supported by an NSERC E.W.R. Steacie Fellowship and under the NSERC Discovery Grant Program.

C. McCreesh– This work was supported by the Engineering and Physical Sciences Research Council (grant number EP/K503058/1).

C. Solnon – This work has been supported by the ANR project SoLStiCe (ANR-13-BS02-0002-01).

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Notes

  1. 1.

    http://liris.cnrs.fr/csolnon/SIP.html.

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Correspondence to Ciaran McCreesh .

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Kotthoff, L., McCreesh, C., Solnon, C. (2016). Portfolios of Subgraph Isomorphism Algorithms. In: Festa, P., Sellmann, M., Vanschoren, J. (eds) Learning and Intelligent Optimization. LION 2016. Lecture Notes in Computer Science(), vol 10079. Springer, Cham. https://doi.org/10.1007/978-3-319-50349-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-50349-3_8

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