Efficient Accelerated Graph Edit Distance Computation on GPU

TL;DR

FAST-GED is a GPU-accelerated framework that significantly speeds up graph edit distance computations, enabling scalable and accurate analysis for large graphs in various domains.

Contribution

The paper introduces FAST-GED, a GPU-based framework that combines high accuracy with fast execution for scalable graph edit distance computation.

Findings

Achieves several orders of magnitude speedup over NetworkX.

Reaches optimal solutions in most cases tested.

Outperforms existing approximate methods in accuracy and scalability.

Abstract

Graph representation is a powerful abstraction of real-world objects and relations. Computing the Graph Edit Distance (GED) between graphs is critical in domains such as bioinformatics, machine learning, and pattern recognition. GED measures the minimum number of edit operations required to transform one graph into another. However, the high computational complexity of optimal and near-optimal methods limits their applicability to large-scale graphs, making high-performance parallel GED computation essential. To address this, we propose FAST-GED, a fast and scalable open-source framework for GED computation on GPUs. FAST-GED overcomes existing limitations by combining high accuracy with fast execution through GPU-friendly algorithmic design and efficient mapping to GPU hardware, minimizing host-device communication. The implementation is optimized and tested across multiple GPU architectures. We validate FAST-GED on real and synthetic datasets with diverse graph sizes and densities. It achieves speedups of several orders of magnitude over the Python NetworkX library while reaching optimal solutions in most cases. Moreover, it outperforms state-of-the-art approximate methods in both accuracy and scalability. We show that FAST-GED enables broader adoption of GED-based solutions in real-world applications.