Efficient Accelerated Graph Edit Distance Computation on GPU
New GPU framework FAST-GED outperforms Python NetworkX by multiple orders of magnitude.
Computing Graph Edit Distance (GED) — the minimum edit operations to transform one graph into another — is critical in bioinformatics, machine learning, and pattern recognition. However, the high computational complexity of optimal and near-optimal methods limits their applicability to large-scale graphs. To address this, researchers Adel Dabah and Andreas Herten 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. Validated 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.
The implementation is optimized and tested across multiple GPU architectures. This work was accepted at the 26th International Conference on Computational Science (ICCS 2026) in Hamburg, Germany. By making GED computation practical for large-scale graphs, FAST-GED enables broader adoption in real-world applications such as drug discovery, social network analysis, and computer vision.
- FAST-GED achieves speedups of several orders of magnitude over Python NetworkX for Graph Edit Distance computation.
- It reaches optimal solutions in most cases while outperforming state-of-the-art approximate methods in accuracy and scalability.
- The open-source framework is optimized for multiple GPU architectures and minimizes host-device communication.
Why It Matters
FAST-GED makes large-scale graph similarity search practical for bioinformatics, fraud detection, and machine learning pipelines.