Distributed Subgraph Matching on Timely Dataflow
Summary: Four distributed subgraph-matching strategies and three general-purpose optimizations identified and implemented on Timely dataflow for unified, strategy-level comparison. Extensive experiments on unlabelled and labelled matching across settings yield a practical guide for selecting approaches. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Longbin Lai (University of New South Wales)
- 2. Zhu Qing (East China Normal University)
- 3. Zhengyi Yang (East China Normal University; University of New South Wales)
- 4. Xin Jin (East China Normal University)
- 5. Zhengmin Lai (East China Normal University)
- 6. Ran Wang (East China Normal University)
- 7. Kongzhang Hao (University of New South Wales)
- 8. Xuemin Lin (East China Normal University; University of New South Wales; Zhejiang Lab)
- 9. Lu Qin (University of Technology Sydney)
- 10. Wenjie Zhang (University of New South Wales)
- 11. Ying Zhang (University of Technology Sydney)
- 12. Zhengping Qian (Alibaba)
- 13. Jingren Zhou (Alibaba)
BibTeX Citation
@article{lai_vldb19,
title = {{Distributed Subgraph Matching on Timely Dataflow}},
author = {Lai, Longbin and Qing, Zhu and Yang, Zhengyi and Jin, Xin and Lai, Zhengmin and Wang, Ran and Hao, Kongzhang and Lin, Xuemin and Qin, Lu and Zhang, Wenjie and Zhang, Ying and Qian, Zhengping and Zhou, Jingren},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {10},
pages = {1099--1112},
doi = {10.14778/3339490.3339494},
url = {https://doi.org/10.14778/3339490.3339494},
year = {2019}
}
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