Pith. sign in

REVIEW 13 cited by

Supervised Learning on Relational Databases with Graph Neural Networks

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2002.02046 v1 pith:MH2PQM2J submitted 2020-02-06 cs.LG cs.AIcs.DBstat.ML

Milan Cvitkovic This is my paper
classification cs.LGcs.AIcs.DBstat.ML
keywords datarelationallearningdatabaseseffortsengineeringfeaturegraph
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The majority of data scientists and machine learning practitioners use relational data in their work [State of ML and Data Science 2017, Kaggle, Inc.]. But training machine learning models on data stored in relational databases requires significant data extraction and feature engineering efforts. These efforts are not only costly, but they also destroy potentially important relational structure in the data. We introduce a method that uses Graph Neural Networks to overcome these challenges. Our proposed method outperforms state-of-the-art automatic feature engineering methods on two out of three datasets.

Discussion (0). Sign in to comment.

Forward citations

Cited by 13 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. From Schema to Signal: Retrieval-Augmented Modeling for Relational Data Analytics

    cs.DB 2026-05 unverdicted novelty 7.0 of 10

    RAM augments relational graph models with attribute-semantic retrieval via random-walk documents and two contrastive augmentations (ATRA, ETRA) to achieve state-of-the-art results on five real-world databases.

  2. Parameter-Free Encoders Remain Viable for RDB Foundation Models

    cs.LG 2026-07 accept novelty 6.0 of 10

    Trainable RDB encoders cannot robustly exploit neighborhood labels as fixed foundation-model features or feature-importance signals, so simple parameter-free encoders stay near-SOTA.

  3. A Fair Benchmarking of Deep Relational Database Learning Models

    cs.DB 2026-07 conditional novelty 6.0 of 10

    Under a unified protocol on five RelBench databases, the Relational Transformer outperforms graph-based RDB models and TabPFN 2.5, with multi-hop gains that shrink relative to training cost.

  4. Universal Encoders for Modular Relational Deep Learning

    cs.LG 2026-06 unverdicted novelty 6.0 of 10

    Proposes a pretrained Universal Row Encoder using transformers and global statistics to generate table-width invariant row embeddings for modular relational graph models, claiming improved transfer, convergence, and m...

  5. What Makes a Desired Graph for Relational Deep Learning?

    cs.AI 2026-06 unverdicted novelty 6.0 of 10

    Schema-derived graphs for relational deep learning suffer from information overload and semantic fragmentation; controlled filtering and injection via an end-to-end optimizer improves accuracy on 26 tasks while often ...

  6. RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases

    cs.LG 2026-05 unverdicted novelty 6.0 of 10

    RelPrism generates self-supervised pseudo-tasks from three attribute perspectives via multi-granularity clustering to improve representation learning for relational database prediction tasks.

  7. Gaussian Relational Graph Transformer

    cs.LG 2026-05 unverdicted novelty 6.0 of 10

    GelGT proposes collaborative sampling and Gaussian attention on subgraphs to model long-range structural, semantic, and temporal dependencies in relational graphs, reporting up to 13.8% gains on downstream tasks.

  8. No Need to Train Your RDB Foundation Model

    cs.AI 2026-02 conditional novelty 6.0 of 10

    Column-wise, parameter-free JUICE encodings let single-table ICL models solve multi-table RDB prediction tasks with no training or fine-tuning.

  9. Parameter-Free Encoders Remain Viable for RDB Foundation Models

    cs.LG 2026-07 conditional novelty 5.0 of 10

    A parameter-free relational encoder combined with a single-table foundation model matches or beats parameterized, pre-trained RDB encoders on six benchmarks.

  10. Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases

    cs.DB 2025-08 unverdicted novelty 5.0 of 10

    SRP combines feature synthesis, cross-table retrieval, and graph propagation to improve predictive modeling on relational databases.

  11. Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases

    cs.DB 2025-07 reject novelty 5.0 of 10

    Rel-HNN models each tuple as a hyperedge over attribute-value nodes and reports large accuracy gains, but its hypergraph construction appears to include the target label as an input node.

  12. Retrieval-Augmented Generation with Graphs (GraphRAG)

    cs.IR 2024-12 unverdicted novelty 5.0 of 10

    A survey proposing a holistic GraphRAG framework with components including query processor, retriever, organizer, generator, and data source, plus domain-tailored reviews, challenges, and future directions.

  13. Graph-Based Feature Augmentation for Predictive Tasks on Relational Datasets

    cs.DB 2025-08 conditional novelty 4.0 of 10

    ReCoGNN automatically augments a base table with task-relevant features from related relational tables by splitting attributes into semantic sub-tables and propagating information through a weighted heterogeneous graph.

Pith tools