Distributed Evaluation of Subgraph Queries Using Worst-case Optimal Low-Memory Dataflows
Summary: Distributed worst-case-optimal dataflows for dynamic subgraph queries and equi-joins, with linear total memory and provably linear per-worker scaling despite adversarial skew. Implemented in Timely Dataflow and evaluated on graphs up to 64B edges. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Khaled Ammar (University of Waterloo)
- 2. Frank McSherry (ETH Zurich)
- 3. Semih Salihoglu (University of Waterloo)
- 4. Manas Joglekar (Google)
BibTeX Citation
@article{ammar_vldb18,
title = {{Distributed Evaluation of Subgraph Queries Using Worst-case Optimal Low-Memory Dataflows}},
author = {Ammar, Khaled and McSherry, Frank and Salihoglu, Semih and Joglekar, Manas},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {6},
pages = {691--704},
doi = {10.14778/3184470.3184473},
url = {https://doi.org/10.14778/3184470.3184473},
year = {2018}
}
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