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 and application in such an emerging field necessitates the development of new tools to compose TGNN models and unify their different schemes for dealing with temporal graphs. In this work, 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：在过去几年中，图神经网络（GNNs）已成为处理（静态）图结构数据的强大且实用的工具。然而，许多实际应用（如社交网络和电子商务）涉及节点和边动态演化的时序图。时序图神经网络（TGNNs）作为GNNs的扩展逐步涌现以处理随时间演变的图，并逐渐成为学术界和工业界的热门研究方向。推动这一新兴领域的研究与应用需要开发新工具来构建TGNN模型并统一其处理时序图的不同方案。本文提出LasTGL——一个集成通用且可扩展的常见时序图学习算法实现以服务于各类高级任务的工业框架。LasTGL旨在提供解决时序图学习任务的基础构建模块，重点关注PyTorch所倡导的易用性和快速原型开发原则。具体而言，LasTGL提供全面的时序图数据集、TGNN模型和实用工具，并配有文档完善的教程，既适合完全初学者，也适用于经验丰富的深度学习从业者。