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)
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Authors
- 1. Hanchen Wang
- 2. Ying Zhang
- 3. Wenjie Zhang
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