Accelerating Dynamic Graph Clustering on GPU Architectures with cuGraph

2026-08-04

AI Summary

This work addresses the high computational cost of community detection in dynamic graphs by proposing an efficient GPU-accelerated method for temporal network analysis. Leveraging the NVIDIA RAPIDS ecosystem, it presents the first GPU-based implementation of modularity optimization and symmetric Bethe–Hessian operator eigendecomposition across multiple graph snapshots. The approach integrates the Leiden algorithm with Dask’s distributed scheduling framework to enable scalable community tracking in large-scale temporal networks. Designed with compatibility for the NetworkX-Temporal interface, the system seamlessly fits into existing graph analytics pipelines. Experimental results demonstrate up to a 1000× speedup over CPU baselines under equivalent workloads, substantially enhancing the efficiency of temporal network analysis in domains such as epidemic spreading, financial systems, and cybersecurity.

Abstract

This work addresses community detection in temporal networks through GPU-accelerated extensions of spectral clustering and modularity-based algorithms originally designed for static graphs. Built on the NVIDIA RAPIDS ecosystem, the framework enables the characterization and tracking of communities in snapshot-based dynamic graphs, either by Leiden greedy optimization with multi-GPU support via Dask-based workload distribution, or eigendecomposition of a symmetric Bethe-Hessian operator. Our multislice modularity backend achieves up to roughly three orders of magnitude speedup over the CPU reference under an equal-work budget, depending on graph density and snapshot count, while preserving compatibility with existing graph analytics pipelines. We demonstrate its applicability on real-world and synthetic datasets, facilitating exploratory analysis of structural network properties over time. Such capabilities are relevant across several application domains, such as epidemic spreading, financial systems, cybersecurity, and trajectory and mobility analysis. We release our implementation as free and open-source software, including Python bindings through the NetworkX-Temporal library for ease of use and zero-code acceleration with existing codebases.

Problem

• dynamic graph clustering
• community detection
• temporal networks
• GPU acceleration

Innovation

• GPU acceleration
• dynamic graph clustering
• multislice modularity
• Bethe-Hessian operator
• multi-GPU Leiden