Graph Neural Networks for Tabular Data Learning

Cheng Te Li, Yu Che Tsai, Jay Chiehen Liao

研究成果: Conference contribution

摘要

Deep learning-based approaches to Tabular Data Learning (TDL) have shown promising performance compared to their conventional counterparts. However, these methods often fail to account for the latent correlation among data instances and feature values. Recently, graph neural networks (GNNs) have gained attention across various application domains, including TDL, for their ability to model relations and interactions between different data entities. By creating appropriate graph structures from the input tabular data and employing GNNs for learning, the performance of TDL can be improved significantly. In this tutorial, we systematically introduce the methodologies of designing and applying GNNs to TDL. Our discussion covers the foundations and overview of GNN-based TDL methods, with a focus on formulating TDL as different graph structures. We also provide a comprehensive taxonomy of constructing graph structures and representation learning in GNN-based TDL methods. We describe the TDL model training framework, which includes different auxiliary tasks and supports open-world learning. Additionally, we discuss how to apply GNNs to various TDL application scenarios and tasks. Finally, we outline the limitations of current research and future directions for this field.

原文English
主出版物標題Proceedings - 2023 IEEE 39th International Conference on Data Engineering, ICDE 2023
發行者IEEE Computer Society
頁面3589-3592
頁數4
ISBN(電子)9798350322279
DOIs
出版狀態Published - 2023
事件39th IEEE International Conference on Data Engineering, ICDE 2023 - Anaheim, United States
持續時間: 2023 4月 32023 4月 7

出版系列

名字Proceedings - International Conference on Data Engineering
2023-April
ISSN(列印)1084-4627

Conference

Conference39th IEEE International Conference on Data Engineering, ICDE 2023
國家/地區United States
城市Anaheim
期間23-04-0323-04-07

All Science Journal Classification (ASJC) codes

  • 軟體
  • 訊號處理
  • 資訊系統

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