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A Query Language Perspective on Graph Learning

Summary: Frames graph/relational representation learning as a query language mapping structures to vectors, unifying GNNs and relational encoders. Recasts expressive-power (distinguishability, approximation) results via this lens and outlines DB-centric research connecting query theory with graph ML. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1902
Venue
PODS
Year
2023
Pagerank
5.2415551e-05
Overall Rank
5,994 | 58.31%
DOI
10.1145/3584372.3589936

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Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
10,663 Inference-friendly Graph Compression for Graph Neural Networks 2025 VLDB 4.1945683e-05
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