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CluStRE: Streaming Graph Clustering with Multi-Stage Refinement

Authors Adil Chhabra , Shai Dorian Peretz , Christian Schulz

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  • Theory of computation → Streaming, sublinear and near linear time algorithms
  • Theory of computation → Graph algorithms analysis
Keywords
  • graph clustering
  • community
  • streaming
  • online
  • memetic
  • evolutionary

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Abstract

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× 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.

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Adil Chhabra, Shai Dorian Peretz, and Christian Schulz. CluStRE: Streaming Graph Clustering with Multi-Stage Refinement. In 23rd International Symposium on Experimental Algorithms (SEA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 338, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.SEA.2025.11

Author Details

Adil Chhabra
  • Heidelberg University, Germany
Shai Dorian Peretz
  • Heidelberg University, Germany
Christian Schulz
  • Heidelberg University, Germany

Funding

We acknowledge support by DFG grant SCHU 2567/5-1.

Acknowledgements

We would like to acknowledge Dagstuhl Seminar 23331 on Recent Trends in Graph Decomposition.

Supplementary Materials

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