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Machine Learning for Graph Data Management and Query Processing

Summary: Comprehensive survey/tutorial of ML methods for graph databases, covering data management (quality, graph generation) and query processing (RL-based planning, deep cardinality estimation, subgraph isomorphism, similarity, community search). Highlights ML gains in latency and generalization but stresses unique DB challenges—scalability, adaptability, robustness—and outlines open research directions. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
14189
Venue
VLDB
Year
2025
Pagerank
4.1945683e-05
Overall Rank
10,843 | 24.57%
DOI
10.14778/3750601.3750702

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