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LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning

arXiv 2311.16605 ↗First posted 28 Nov 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 41 of its 55 references that are in the map. First posted on arXiv 28 Nov 2023.

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41 of 55 references.

References · 41

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29 more
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