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GammaGL: A Multi-Backend Library for Graph Neural Networks

Published: 18 July 2023 Publication History

Abstract

Graph Neural Networks (GNNs) have shown their superiority in modeling graph-structured data, and gained much attention over the last five years. Though traditional deep learning frameworks such as TensorFlow and PyTorch provide convenient tools for implementing neural network algorithms, they do not support the key operations of GNNs well, e.g., the message passing computation based on sparse matrices. To address this issue, GNN libraries such as PyG are proposed by introducing rich Application Programming Interfaces (APIs) specialized for GNNs. However, most current GNN libraries only support a specific deep learning framework as the backend, e.g., PyG is tied up with PyTorch. In practice, users usually need to combine GNNs with other neural network components, which may come from their co-workers or open-source codes with different deep-learning backends. Consequently, users have to be familiar with various GNN libraries, and rewrite their GNNs with corresponding APIs. To provide a more convenient user experience, we present Gamma Graph Library (GammaGL), a GNN library that supports multiple deep learning frameworks as backends. GammaGL uses a framework-agnostic design that allows users to easily switch between deep learning backends on top of existing components with a single line of code change. Following the tensor-centric design idea, GammaGL splits the graph data into several key tensors, and abstracts GNN computational processes (such as message passing and graph mini-batch operations) into a few key functions. We develop many efficient operators in GammaGL for acceleration. So far, GammaGL has provided more than 40 GNN examples that can be applied to a variety of downstream tasks. GammaGL also provides tools for heterogeneous graph neural networks and recommendations to facilitate research in related fields. We present the performance of models implemented by GammaGL and the time consumption of our optimized operators to show the efficiency. Our library is available at https://github.com/BUPT-GAMMA/GammaGL.

Supplemental Material

MP4 File
GammaGL provides users with multiple choices of deep-learning frameworks for implementing Graph Neural Networks. In this video, we give a brief introduction of the GammaGL. We are willing to learn from the users' advice and further extend our library in the future.

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cover image ACM Conferences
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2023
3567 pages
ISBN:9781450394086
DOI:10.1145/3539618
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Published: 18 July 2023

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Author Tags

  1. deep learning
  2. frameworks
  3. graph neural networks

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  • The National Natural Science Foundation of China

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Cited By

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  • (2024)Endowing Pre-trained Graph Models with Provable FairnessProceedings of the ACM Web Conference 202410.1145/3589334.3645703(1045-1056)Online publication date: 13-May-2024
  • (2024)Federated Heterogeneous Graph Neural Network for Privacy-preserving RecommendationProceedings of the ACM Web Conference 202410.1145/3589334.3645693(3919-3929)Online publication date: 13-May-2024
  • (2023)Provable training for graph contrastive learningProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668311(50327-50345)Online publication date: 10-Dec-2023

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