Machine Learning for Subgraph Extraction: Methods, Applications and Challenges

Summary: Survey of ML-based approaches for subgraph extraction covering subgraph isomorphism, maximum common subgraph, community detection and community search; contrasts learning methods with classical algorithms in efficiency, scalability and effectiveness. Analyzes model designs, empirical performance, applications, datasets and open challenges to guide future database research. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13215
Venue: VLDB
Year: 2023
Pagerank: 4.6089395e-05
Overall Rank: 7,939 | 44.83%
DOI: 10.14778/3611540.3611571

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,847	Machine Learning for Graph Data Management and Query Processing	2025	VLDB	4.1905499e-05

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
2,901	ICS-GNN: Lightweight Interactive Community Search via Graph Neural Network	2021	VLDB	7.9448215e-05
2,988	Neural Subgraph Counting with Wasserstein Estimator	2022	SIGMOD	7.7752463e-05
3,370	Query Driven-Graph Neural Networks for Community Search: From Non-Attributed, Attributed, to Interactive Attributed	2022	VLDB	7.1645471e-05
3,781	A Learned Sketch for Subgraph Counting	2021	SIGMOD	6.7691344e-05

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