Relational Deep Learning: Graph Representation Learning on Relational Databases

• URL: http://arxiv.org/abs/2312.04615v1
• Date: Thu, 7 Dec 2023 18:51:41 GMT
• Title: Relational Deep Learning: Graph Representation Learning on Relational Databases
• Authors: Matthias Fey, Weihua Hu, Kexin Huang, Jan Eric Lenssen, Rishabh Ranjan, Joshua Robinson, Rex Ying, Jiaxuan You, Jure Leskovec
• Abstract summary: We introduce an end-to-end representation approach to learn on data laid out across multiple tables. Message Passing Graph Neural Networks can then automatically learn across the graph to extract representations that leverage all data input.
• Score: 69.7008152388055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Much of the world's most valued data is stored in relational databases and data warehouses, where the data is organized into many tables connected by primary-foreign key relations. However, building machine learning models using this data is both challenging and time consuming. The core problem is that no machine learning method is capable of learning on multiple tables interconnected by primary-foreign key relations. Current methods can only learn from a single table, so the data must first be manually joined and aggregated into a single training table, the process known as feature engineering. Feature engineering is slow, error prone and leads to suboptimal models. Here we introduce an end-to-end deep representation learning approach to directly learn on data laid out across multiple tables. We name our approach Relational Deep Learning (RDL). The core idea is to view relational databases as a temporal, heterogeneous graph, with a node for each row in each table, and edges specified by primary-foreign key links. Message Passing Graph Neural Networks can then automatically learn across the graph to extract representations that leverage all input data, without any manual feature engineering. Relational Deep Learning leads to more accurate models that can be built much faster. To facilitate research in this area, we develop RelBench, a set of benchmark datasets and an implementation of Relational Deep Learning. The data covers a wide spectrum, from discussions on Stack Exchange to book reviews on the Amazon Product Catalog. Overall, we define a new research area that generalizes graph machine learning and broadens its applicability to a wide set of AI use cases.

Related papers

• RelBench: A Benchmark for Deep Learning on Relational Databases [78.52438155603781]
  We present RelBench, a public benchmark for solving tasks over databases with graph neural networks. We use RelBench to conduct the first comprehensive study of Deep Learning infrastructure. RDL learns better whilst reducing human work needed by more than an order of magnitude.
  arXiv  Detail & Related papers  (2024-07-29T14:46:13Z)
• GFS: Graph-based Feature Synthesis for Prediction over Relational Databases [39.975491511390985]
  We propose a novel framework called Graph-based Feature Synthesis (GFS) GFS formulates relational database as a heterogeneous graph database. In an experiment over four real-world multi-table relational databases, GFS outperforms previous methods designed for relational databases.
  arXiv  Detail & Related papers  (2023-12-04T16:54:40Z)
• Graph Neural Network contextual embedding for Deep Learning on Tabular Data [0.45880283710344055]
  Deep Learning (DL) has constituted a major breakthrough for AI in fields related to human skills like natural language processing. This paper presents a novel DL model using Graph Neural Network (GNN) more specifically Interaction Network (IN) Its results outperform those of a recently published survey with DL benchmark based on five public datasets, also achieving competitive results when compared to boosted-tree solutions.
  arXiv  Detail & Related papers  (2023-03-11T17:13:24Z)
• ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy [65.5580334698777]
  ViRel is a method for unsupervised discovery and learning of Visual Relations with graph-level analogy. We show that our method achieves above 95% accuracy in relation classification. We further generalizes to unseen tasks with more complicated relational structures.
  arXiv  Detail & Related papers  (2022-07-04T16:56:45Z)
• Deep Transfer Learning for Multi-source Entity Linkage via Domain Adaptation [63.24594955429465]
  Multi-source entity linkage is critical in high-impact applications such as data cleaning and user stitching. AdaMEL is a deep transfer learning framework that learns generic high-level knowledge to perform multi-source entity linkage. Our framework achieves state-of-the-art results with 8.21% improvement on average over methods based on supervised learning.
  arXiv  Detail & Related papers  (2021-10-27T15:20:41Z)
• HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression [53.90578309960526]
  Large pre-trained language models (PLMs) have shown overwhelming performances compared with traditional neural network methods. We propose a hierarchical relational knowledge distillation (HRKD) method to capture both hierarchical and domain relational information.
  arXiv  Detail & Related papers  (2021-10-16T11:23:02Z)
• A Lightweight Algorithm to Uncover Deep Relationships in Data Tables [16.829302261959924]
  We develop automated methods to uncover deep relationships in a single data table without expert or domain knowledge. Our method can decompose a data table into layers of smaller tables, revealing its deep structure. With our solution, data scientists will be provided with automatically generated, data-driven insights when exploring new data sets.
  arXiv  Detail & Related papers  (2020-09-07T18:25:15Z)
• Relation-Guided Representation Learning [53.60351496449232]
  We propose a new representation learning method that explicitly models and leverages sample relations. Our framework well preserves the relations between samples. By seeking to embed samples into subspace, we show that our method can address the large-scale and out-of-sample problem.
  arXiv  Detail & Related papers  (2020-07-11T10:57:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.