{"total":45,"rows":[{"id":"2407.20060","title":"RelBench: A Benchmark for Deep Learning on Relational Databases","created":"2024-07-29","cited_by_count":96,"area":null},{"id":"2312.04615","title":"Relational Deep Learning: Graph Representation Learning on Relational Databases","created":"2023-12-07","cited_by_count":56,"area":null},{"id":"2502.06784","title":"RelGNN: Composite Message Passing for Relational Deep Learning","created":"2025-02-10","cited_by_count":48,"area":null},{"id":"2505.05568","title":"Griffin: Towards a Graph-Centric Relational Database Foundation Model","created":"2025-05-08","cited_by_count":47,"area":null},{"id":"2107.01830","title":"ARM-Net: Adaptive Relation Modeling Network for Structured Data","created":"2021-07-05","cited_by_count":24,"area":null},{"id":"2312.02037","title":"GFS: Graph-based Feature Synthesis for Prediction over Relational Databases","created":"2023-12-04","cited_by_count":16,"area":null},{"id":"2603.03805","title":"Relational In-Context Learning via Synthetic Pre-training with Structural Prior","created":"2026-03-04","cited_by_count":14,"area":null},{"id":"2401.02143","title":"Graph Neural Networks for Tabular Data Learning: A Survey with Taxonomy and Directions","created":"2024-01-04","cited_by_count":13,"area":null},{"id":"2404.18209","title":"4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs","created":"2024-04-28","cited_by_count":11,"area":null},{"id":"2406.14541","title":"Why LLMs Are Bad at Synthetic Table Generation (and what to do about it)","created":"2024-06-20","cited_by_count":10,"area":null},{"id":"2602.13697","title":"No Need to Train Your RDB Foundation Model","created":"2026-02-14","cited_by_count":10,"area":null},{"id":"2412.05218","title":"Transformers Meet Relational Databases","created":"2024-12-06","cited_by_count":9,"area":null},{"id":"2211.16889","title":"Generating Realistic Synthetic Relational Data through Graph Variational Autoencoders","created":"2022-11-30","cited_by_count":8,"area":null},{"id":"2303.06455","title":"Graph Neural Network contextual embedding for Deep Learning on Tabular Data","created":"2023-03-11","cited_by_count":8,"area":null},{"id":"2506.01360","title":"RDB2G-Bench: A Comprehensive Benchmark for Automatic Graph Modeling of Relational Databases","created":"2025-06-02","cited_by_count":8,"area":null},{"id":"2506.22199","title":"REDELEX: A Framework for Relational Deep Learning Exploration","created":"2025-06-27","cited_by_count":8,"area":null},{"id":"2305.15321","title":"Towards Foundation Models for Relational Databases [Vision Paper]","created":"2023-05-24","cited_by_count":7,"area":null},{"id":"2408.12185","title":"Rank and Align: Towards Effective Source-free Graph Domain Adaptation","created":"2024-08-22","cited_by_count":7,"area":null},{"id":"2501.15282","title":"AutoG: Towards automatic graph construction from tabular data","created":"2025-01-25","cited_by_count":7,"area":null},{"id":"2506.02243","title":"From Features to Structure: Task-Aware Graph Construction for Relational and Tabular Learning with GNNs","created":"2025-06-02","cited_by_count":6,"area":null},{"id":"2602.18495","title":"RDBLearn: Simple In-Context Prediction Over Relational Databases","created":"2026-02-14","cited_by_count":5,"area":null},{"id":"2506.22530","title":"Task-Agnostic Contrastive Pretraining for Relational Deep Learning","created":"2025-06-27","cited_by_count":4,"area":null},{"id":"2507.09837","title":"A Pre-training Framework for Relational Data with Information-theoretic Principles","created":"2025-07-14","cited_by_count":4,"area":null},{"id":"2310.16837","title":"RDBench: ML Benchmark for Relational Databases","created":"2023-10-25","cited_by_count":3,"area":null},{"id":"2211.05213","title":"Flaky Performances when Pretraining on Relational Databases","created":"2022-11-09","cited_by_count":2,"area":null},{"id":"2302.11777","title":"Embeddings for Tabular Data: A Survey","created":"2023-02-23","cited_by_count":2,"area":null},{"id":"2405.18670","title":"Differentially Private Synthetic Data Generation for Relational Databases","created":"2024-05-29","cited_by_count":2,"area":null},{"id":"2311.14304","title":"AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine","created":"2023-11-24","cited_by_count":1,"area":null},{"id":"2504.04935","title":"RCCFormer: A Robust Crowd Counting Network Based on Transformer","created":"2025-04-07","cited_by_count":1,"area":null},{"id":"2509.09482","title":"Database Views as Explanations for Relational Deep Learning","created":"2025-09-11","cited_by_count":1,"area":null},{"id":"2510.06980","title":"Relational Database Distillation: From Structured Tables to Condensed Graph Data","created":"2025-10-08","cited_by_count":1,"area":null},{"id":"2605.23241","title":"RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases","created":"2026-05-22","cited_by_count":1,"area":null},{"id":"2607.03659","title":"A Fair Benchmarking of Deep Relational Database Learning Models","created":"2026-07-04","cited_by_count":1,"area":null},{"id":"2201.12843","title":"Graph Representation Learning via Aggregation Enhancement","created":"2022-01-30","cited_by_count":0,"area":null},{"id":"2204.06688","title":"Time Series of Non-Additive Metrics: Identification and Interpretation of Contributing Factors of Variance by Linear Decomposition","created":"2022-04-14","cited_by_count":0,"area":null},{"id":"2507.12562","title":"Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases","created":"2025-07-16","cited_by_count":0,"area":null},{"id":"2508.08327","title":"Synthesize, Retrieve, and Propagate: A Unified Predictive Modeling Framework for Relational Databases","created":"2025-08-10","cited_by_count":0,"area":null},{"id":"2508.20986","title":"Graph-Based Feature Augmentation for Predictive Tasks on Relational Datasets","created":"2025-08-28","cited_by_count":0,"area":null},{"id":"2605.15575","title":"Gaussian Relational Graph Transformer","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2606.08491","title":"What Makes a Desired Graph for Relational Deep Learning?","created":"2026-06-07","cited_by_count":0,"area":null},{"id":"2606.21434","title":"Universal Encoders for Modular Relational Deep Learning","created":"2026-06-19","cited_by_count":0,"area":null},{"id":"2607.05478","title":"InvWeaver: Deductive Feedback for Invariant Synthesis in Interacting-Loop Programs","created":"2026-07-06","cited_by_count":0,"area":null},{"id":"2608.13023","title":"Incremental Evaluation and Training in Relational Deep Learning","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2609.19832","title":"MetaRTL: Meta-path Attention Enhanced Relational Table Learning","created":"2026-09-17","cited_by_count":0,"area":null},{"id":"2610.01725","title":"RelICL: Training-free Relational Learning with Tabular Foundation Models","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}