  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.
