SPACE: Cardinality Estimation for Path Queries Using Cardinality-Aware Sequence-based Learning
Summary: SPACE models graph path patterns as sequences of node labels and edge types, learning their cardinalities with dual encodings. Dual sequence and cardinality-aware encodings yield accurate estimates and faster training than prior methods. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Mehmet Aytimur (University of Konstanz)
- 2. Theodoros Chondrogiannis (Norwegian Institute of Technology; University of Konstanz)
- 3. Michael Grossniklaus (Thurgau Institute for Digital Transformation; University of Konstanz)
BibTeX Citation
@inproceedings{aytimur_sigmod25,
title = {{SPACE: Cardinality Estimation for Path Queries Using Cardinality-Aware Sequence-based Learning}},
author = {Aytimur, Mehmet and Chondrogiannis, Theodoros and Grossniklaus, Michael},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725355},
url = {https://dl.acm.org/doi/10.1145/3725355},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,640 | Enumerating Graph Pattern Matches with ML Oracles | 2026 | SIGMOD | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 27 of 27 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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