A Scalable AutoML Approach Based on Graph Neural Networks

Summary: KGpip: scalable AutoML meta-learning using graph neural networks. Constructs a dataset-pipeline graph DB by mining scripts, uses content embeddings to find similar datasets, and frames AutoML pipeline generation as graph generation; outperforms SOTA on 121 datasets. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12734
Venue: VLDB
Year: 2022
Pagerank: 5.5779335e-05
Overall Rank: 5,304 | 63.11%
DOI: 10.14778/3551793.3551804

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
7,494	SubStrat: A Subset-Based Optimization Strategy for Faster AutoML	2023	VLDB	4.7180617e-05
10,628	CatDB: Data-catalog-guided, LLM-based Generation of Data-centric ML Pipelines	2025	VLDB	4.1945683e-05
11,086	FedGTA: Topology-aware Averaging for Federated Graph Learning	2024	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
2,384	Oracle AutoML: A Fast and Predictive AutoML Pipeline	2020	VLDB	8.925354e-05
2,839	VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition	2021	VLDB	8.0378978e-05

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