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Approximate Graph Edit Distance in Quadratic Time.

Kaspar Riesen, Miquel Ferrer, Horst Bunke

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    This summary is machine-generated.

    This study introduces faster, approximate graph edit distance algorithms. Greedy methods reduce computation time from cubic to quadratic, enabling accurate graph matching for large datasets in pattern classification.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Graph Theory

    Background:

    • Graph edit distance (GED) is a flexible graph matching model.
    • High computational complexity limits GED's application to small graphs.
    • Existing approximation frameworks offer O(n^3) time complexity.

    Purpose of the Study:

    • To develop faster approximation algorithms for graph edit distance.
    • To reduce the time complexity for large-scale graph matching.
    • To maintain accuracy for graph-based pattern classification.

    Main Methods:

    • Introduced a general approximation framework for graph edit distance.
    • Proposed using suboptimal algorithms with quadratic time complexity.
    • Developed and evaluated five different greedy assignment algorithms.

    Main Results:

    • The proposed greedy algorithms achieve quadratic time complexity.
    • Experimental evaluation demonstrates significant speed-up for graph edit distance computation.
    • Approximated distances remain sufficiently accurate for pattern classification.

    Conclusions:

    • Greedy assignment algorithms offer a promising approach to accelerate graph edit distance computation.
    • These methods enhance the applicability of graph matching to larger datasets.
    • The trade-off between speed and accuracy is suitable for graph-based pattern classification.