发表机构
University of Pisa; National Research Council（比萨大学; 国家研究委员会）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究基于NVIDIA RAPIDS生态系统，将静态图的谱聚类与模块度算法扩展至GPU，实现动态图社区检测，在相同计算资源下较CPU加速约三个数量级，支持多GPU，且为开源软件。
AI 中文摘要
本研究针对时间网络中的社区检测问题，对原本针对静态图设计的谱聚类和基于模块度的算法进行GPU加速扩展。该框架基于NVIDIA RAPIDS生态系统构建，可对基于快照的动态图中的社区进行表征与跟踪，支持两种方式：一是通过基于Dask的工作负载分配实现多GPU支持的Leiden贪婪优化，二是对称Bethe-Hessian算子的特征分解。我们的多切片模块度后端在相同计算资源预算下，相较于CPU参考实现实现了约三个数量级的加速，加速幅度取决于图密度和快照数量，同时保持了与现有图分析流程的兼容性。我们在真实世界和合成数据集上验证了其适用性，助力对随时间变化的网络结构属性进行探索性分析，该能力适用于疫情传播、金融系统、网络安全、轨迹与移动性分析等多个应用领域。我们将实现作为自由开源软件发布，包含通过NetworkX-Temporal库提供的Python绑定，便于使用并可对现有代码库实现零代码加速。
英文摘要
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.
Comments12 pages, 2 figures. Accepted at FRAME 2026, Euro-Par 2026 Workshops; to appear in Springer LNCS
