用GPU加速动态图聚类，速度提升千倍以上。
Accelerating Dynamic Graph Clustering on GPU Architectures with cuGraph
- 基于GPU的谱聚类与模块度算法，支持多卡并行计算。
- 相比CPU基准，最高提速近三个数量级，依赖图密度和快照数。
- 兼容现有分析流程，适合网络演化研究者使用。
本文针对时序网络中的社区检测问题，提出基于GPU加速的谱聚类与基于模块度的算法扩展，适用于静态图设计的算法。依托NVIDIA RAPIDS生态，该框架支持基于快照的动态图中社区的表征与追踪，采用Leiden贪婪优化结合基于Dask的工作负载分发实现多GPU支持，或通过对对称Bethe-Hessian算子进行特征分解。多切片模块度后端在同等工作量下，相较于CPU参考实现，速度提升可达约三个数量级，具体取决于图密度和快照数量，同时保持与现有图分析流水线的兼容性。我们在真实世界与合成数据集上验证了其有效性，助力对随时间演变的网络结构属性进行探索性分析。该能力在流行病传播、金融系统、网络安全、轨迹与移动性分析等领域具有广泛应用价值。我们已将实现开源，提供NetworkX-Temporal库的Python绑定，支持零代码加速现有代码基。
原文摘要 · Abstract (English)
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.
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