CluStRE流式聚类算法通过多阶段优化，在低内存下实现高质量图聚类。
CluStRE: Streaming Graph Clustering with Multi-Stage Refinement
- 动态构建商图，结合模块度优化与重流机制提升聚类质量。
- 比当前最优流式算法快2.6倍，内存减少超三分之二，质量提升89.8%。
- 适合处理大规模动态图，兼顾速度、内存与聚类精度，接近传统方法效果。
我们提出CluStRE，一种新型流式图聚类算法，通过多阶段精炼在计算效率与聚类质量间取得平衡。与传统内存聚类方法不同，CluStRE在流式环境下处理图数据，显著降低内存开销，同时利用重流和演化启发式策略提升解的质量。该方法动态构建商图，支持基于模块度的优化，高效处理大规模图。我们设计了多种配置，以在速度、内存消耗与聚类质量之间提供权衡。实验表明，CluStRE平均提升解质量89.8%，运行速度达现有最优流式算法的2.6倍，内存使用低于其三分之二；最强模式下解质量平均提升高达150%。最终，其聚类质量超过96%的内存聚类方法（如Louvain），有效弥合了流式与传统聚类方法之间的差距。
原文摘要 · Abstract (English)
We present CluStRE, a novel streaming graph clustering algorithm that balances computational efficiency with high-quality clustering using multi-stage refinement. Unlike traditional in-memory clustering approaches, CluStRE processes graphs in a streaming setting, significantly reducing memory overhead while leveraging re-streaming and evolutionary heuristics to improve solution quality. Our method dynamically constructs a quotient graph, enabling modularity-based optimization while efficiently handling large-scale graphs. We introduce multiple configurations of CluStRE to provide trade-offs between speed, memory consumption, and clustering quality. Experimental evaluations demonstrate that CluStRE improves solution quality by 89.8%, operates 2.6 times faster, and uses less than two-thirds of the memory required by the state-of-the-art streaming clustering algorithm on average. Moreover, our strongest mode enhances solution quality by up to 150% on average. With this, CluStRE achieves comparable solution quality to in-memory algorithms, i.e. over 96% of the quality of clustering approaches, including Louvain, effectively bridging the gap between streaming and traditional clustering methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。