Fast Computation of Graph Edit Distance

TL;DR

This paper introduces BSS_GED, a new vertex-based method that significantly improves the efficiency of graph edit distance computation by reducing search space and employing heuristic search, outperforming existing methods.

Contribution

The paper presents BSS_GED, a novel approach that reduces search space and uses beam-stack search with heuristics for faster, more memory-efficient GED computation.

Findings

BSS_GED outperforms state-of-the-art GED methods in efficiency.

It effectively handles both sparse and dense graphs.

BSS_GED is also effective for graph similarity search.

Abstract

The graph edit distance (GED) is a well-established distance measure widely used in many applications. However, existing methods for the GED computation suffer from several drawbacks including oversized search space, huge memory consumption, and lots of expensive backtracking. In this paper, we present BSS_GED, a novel vertex-based mapping method for the GED computation. First, we create a small search space by reducing the number of invalid and redundant mappings involved in the GED computation. Then, we utilize beam-stack search combined with two heuristics to efficiently compute GED, achieving a flexible trade-off between available memory and expensive backtracking. Extensive experiments demonstrate that BSS GED is highly efficient for the GED computation on sparse as well as dense graphs and outperforms the state-of-the-art GED methods. In addition, we also apply BSS_GED to the graph similarity search problem and the practical results confirm its efficiency.