Improved local search for graph edit distance
This work addresses the challenge of efficiently approximating GED for applications like structural pattern recognition, though it is incremental as it builds on existing local search methods.
The paper tackles the NP-hard problem of computing graph edit distance (GED) by introducing K-REFINE, which improves an existing local search algorithm for small graphs, and RANDPOST, a warm start framework for large graphs, with both performing excellently in practice.
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 NP-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.