mlsanityData to 7 Oct 2026

CogDL: A Comprehensive Library for Graph Deep Learning

arXiv 2103.00959 ↗First posted 1 Mar 2021cs.SI cs.LG stat.MLcited by 12

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Abstract (arXiv)

Graph neural networks (GNNs) have attracted tremendous attention from the graph learning community in recent years. It has been widely adopted in various real-world applications from diverse domains, such as social networks and biological graphs. The research and applications of graph deep learning present new challenges, including the sparse nature of graph data, complicated training of GNNs, and non-standard evaluation of graph tasks. To tackle the issues, we present CogDL, a comprehensive library for graph deep learning that allows researchers and practitioners to conduct experiments, compare methods, and build applications with ease and efficiency. In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries. By utilizing this unified trainer, CogDL can optimize the GNN training loop with several training techniques, such as mixed precision training. Moreover, we develop efficient sparse operators for CogDL, enabling it to become the most competitive graph library for efficiency. Another important CogDL feature is its focus on ease of use with the aim of facilitating open and reproducible research of graph learning. We leverage CogDL to report and maintain benchmark results on fundamental graph tasks, which can be reproduced and directly used by the community.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 77 of its 120 references that are in the map. First posted on arXiv 1 Mar 2021.

Builds on

77 of 120 references.

References · 77

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38 more
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50 most cited of 77 All 77 (JSON)

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Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 5; 2025: 2.

12 papers.

  1. 2023Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasetscited by 37
  2. 2024Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Miningcited by 20
  3. 2022PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphscited by 18
  4. 2024PST-Bench: Tracing and Benchmarking the Source of Publicationscited by 7
  5. 2024GraphStorm: all-in-one graph machine learning framework for industry applicationscited by 7
  6. 2025Revisiting Dynamic Graph Clustering via Matrix Factorizationcited by 7
  7. 2023PyTorch Geometric High Order: A Unified Library for High Order Graph Neural Networkcited by 4
  8. 2023A Network Science perspective of Graph Convolutional Networks: A surveycited by 1
  9. 2023Graph Neural Networks with a Distribution of Parametrized Graphscited by 1
  10. 2024SDT-GNN: Streaming-based Distributed Training Framework for Graph Neural Networkscited by 0
  11. 2024Detection of adrenal anomalous findings in spinal CT images using multi model graph aggregationcited by 0
  12. 2025An Efficient Hybridization of Graph Representation Learning and Metaheuristics for the Constrained Incremental Graph Drawing Problemcited by 0