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

• URL: http://arxiv.org/abs/2311.16605v2
• Date: Thu, 30 Nov 2023 09:19:39 GMT
• Title: LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
• Authors: Jintang Li, Jiawang Dan, Ruofan Wu, Jing Zhou, Sheng Tian, Yunfei Liu, Baokun Wang, Changhua Meng, Weiqiang Wang, Yuchang Zhu, Liang Chen, Zibin Zheng
• Abstract summary: We introduce LasTGL, an industrial framework that integrates unified and unified implementations of common temporal graph learning algorithms. LasTGL provides comprehensive temporal graph datasets, TGNN models and utilities along with well-documented tutorials.
• Score: 61.4707298969173
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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