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.

翻译：近年来，图神经网络已成为学习（静态）图结构数据的强大实用工具。然而，许多现实应用（如社交网络和电子商务）涉及节点与边动态演化的时序图。时序图神经网络作为图神经网络的扩展逐步兴起，旨在处理随时间演化的图结构，并已成为学术界与工业界的热门研究方向。推动这一新兴领域的研究需要新工具来构建时序图神经网络模型，并统一处理时序图的不同范式。为促进时序图学习的研究与应用，我们提出LasTGL——一个集成统一且可扩展的通用时序图学习算法实现的工业框架，适用于多种高级任务。LasTGL旨在提供解决时序图学习任务的核心构建模块，聚焦于PyTorch所倡导的易用性与快速原型设计原则。该框架提供全面的时序图数据集、时序图神经网络模型及实用工具，并附有详尽的教程文档，既适合零基础初学者，也适合深度学习领域的专业从业者。