Graph Edit Distance Estimation: A New Heuristic and A Holistic Evaluation of Learning-based Methods

Graph edit distance (GED) is an important metric for measuring the distance or similarity between two graphs. It is defined as the minimum number of edit operations required to transform one graph into another. Computing the exact GED between two graphs is an NP-hard problem. With the success of deep learning across various application domains, graph neural networks have also been recently utilized to predict the GED between graphs. However, the existing studies on learning-based methods have two significant limitations. (1) The development of deep learning models for GED prediction has been explored in various research fields (e.g., databases, machine learning, information retrieval, and computer vision), yet cross-field evaluations have been quite limited. (2) More importantly, all these advancements have been evaluated against a simple combinatorial heuristic baseline, with their models shown to outperform it. In this paper, we aim to bridge this knowledge gap. We first conduct a holistic review of the existing learning-based methods, categorizing them into non-interpretable and interpretable GED prediction approaches, while highlighting their overarching design principles and relationships among these models. Secondly, we present a simple yet effective combinatorial heuristic algorithm App-BMao for GED estimation, adapted from an existing exact GED computation algorithm. App-BMao provides interpretable GED estimation with controlled time and space complexity. Extensive empirical evaluations on three widely used datasets show that the new heuristic algorithm App-BMao outperforms all existing learning-based approaches for both interpretable and non-interpretable GED prediction.