论文标题
通过图神经网络对关系数据库的监督学习
Supervised Learning on Relational Databases with Graph Neural Networks
论文作者
论文摘要
大多数数据科学家和机器学习从业人员在其工作中使用关系数据[ML和Data Science 2017，Kaggle，Inc。]。但是，在关系数据库中存储的数据上的培训机学习模型需要大量的数据提取和功能工程工作。这些努力不仅是昂贵的，而且还破坏了数据中潜在的重要关系结构。我们介绍了一种使用图形神经网络来克服这些挑战的方法。我们所提出的方法优于三个数据集中两个的最先进的自动功能工程方法。
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.
