Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data. However, many real-world applications, such as social networks and e-commerce, involve temporal graphs where nodes and edges are dynamically evolving. Temporal graph neural networks (TGNNs) have progressively emerged as an extension of GNNs to address time-evolving graphs and have gradually become a trending research topic in both academics and industry. Advancing research in such an emerging field requires new tools to compose TGNN models and unify their different schemes in dealing with temporal graphs. To facilitate research and application in temporal graph learning, we introduce LasTGL, an industrial framework that integrates unified and extensible implementations of common temporal graph learning algorithms for various advanced tasks. The purpose of LasTGL is to provide the essential building blocks for solving temporal graph learning tasks, focusing on the guiding principles of user-friendliness and quick prototyping on which PyTorch is based. In particular, LasTGL provides comprehensive temporal graph datasets, TGNN models and utilities along with well-documented tutorials, making it suitable for both absolute beginners and expert deep learning practitioners alike.

LasTGL是一个用于解决时间图学习问题的产业框架，它整合了常见的时间图学习算法的统一和可扩展实现，并提供了全面的时间图数据集、TGNN模型和工具，适用于初学者和专业深度学习从业者。

LasTGL: 大规模时间图学习的工业框架