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On the Variable Ordering in Subgraph Isomorphism Algorithms.

Vincenzo Bonnici, Rosalba Giugno

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |January 14, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study explores variable ordering strategies to speed up subgraph isomorphism algorithms for biological data analysis. Effective ordering significantly reduces computation time for graph matching in biological databases.

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    Area of Science:

    • Computational Biology
    • Graph Theory
    • Bioinformatics

    Background:

    • Biological data can be represented as graphs, necessitating subgraph isomorphism algorithms for analysis.
    • Subgraph isomorphism is an NP-complete problem, posing computational challenges.
    • Efficient algorithms are crucial for handling large biological datasets.

    Purpose of the Study:

    • To investigate strategies for reducing subgraph isomorphism algorithm running time.
    • To emphasize the impact of variable ordering on algorithm efficiency.
    • To compare recent variable ordering solutions with existing methods and contest algorithms.

    Main Methods:

    • Reviewing existing strategies for subgraph isomorphism.
    • Focusing on two recent solutions employing effective variable ordering.
    • Comparing these solutions against other variable ordering strategies and ICPR2014 contest algorithms.

    Main Results:

    • Variable ordering significantly influences the running time of subgraph isomorphism algorithms.
    • Recent solutions with effective variable ordering demonstrate improved performance.
    • Performance is benchmarked against established strategies and contest participants.

    Conclusions:

    • Effective variable ordering is a key factor in optimizing subgraph isomorphism algorithms for biological data.
    • The investigated recent solutions offer promising improvements in computational efficiency.
    • Further research into variable ordering can enhance pattern search in biological databases.