Improved local search for graph edit distance

Nicolas Boria (1) , David Blumenthal (2) , Sébastien Bougleux (1) , Luc Brun (1)

1 Equipe Image - Laboratoire GREYC - UMR6072
2 Free University of Bozen-Bolzano

The graph edit distance (GED) measures the dissimilarity between two graphs as the minimal cost of a sequence of elementary operations transforming one graph into another. This measure is fundamental in many areas such as structural pattern recognition or classification. However, exactly computing GED is -hard. Among different classes of heuristic algorithms that were proposed to compute approximate solutions, local search based algorithms provide the tightest upper bounds for GED. In this paper, we present K-REFINE and RANDPOST. K-REFINE generalizes and improves an existing local search algorithm and performs particularly well on small graphs. RANDPOST is a general warm start framework that stochastically generates promising initial solutions to be used by any local search based GED algorithm. It is particularly efficient on large graphs. An extensive empirical evaluation demonstrates that both K-REFINE and RANDPOST perform excellently in practice.

Mots clés: Graph edit distance; Local search; Stochastic warm start

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Nicolas Boria, David Blumenthal, Sébastien Bougleux, Luc Brun. Improved local search for graph edit distance. Pattern Recognition Letters, 2020, 129, pp.19-25. ⟨10.1016/j.patrec.2019.10.028⟩. ⟨hal-02388317⟩