mlsanityData to 7 Oct 2026

GraphGallery: A Platform for Fast Benchmarking and Easy Development of Graph Neural Networks Based Intelligent Software

arXiv 2102.07933 ↗First posted 16 Feb 2021cs.AI cs.LG cs.SEcited by 8

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Abstract (arXiv)

Graph Neural Networks (GNNs) have recently shown to be powerful tools for representing and analyzing graph data. So far GNNs is becoming an increasingly critical role in software engineering including program analysis, type inference, and code representation. In this paper, we introduce GraphGallery, a platform for fast benchmarking and easy development of GNNs based software. GraphGallery is an easy-to-use platform that allows developers to automatically deploy GNNs even with less domain-specific knowledge. It offers a set of implementations of common GNN models based on mainstream deep learning frameworks. In addition, existing GNNs toolboxes such as PyG and DGL can be easily incorporated into the platform. Experiments demonstrate the reliability of implementations and superiority in fast coding. The official source code of GraphGallery is available at https://github.com/EdisonLeeeee/GraphGallery and a demo video can be found at https://youtu.be/mv7Zs1YeaYo.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 9 of its 18 references that are in the map. First posted on arXiv 16 Feb 2021.

Builds on

9 of 18 references.

References · 9

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  5. 2019Simplifying Graph Convolutional Networkscited by 1,470
  6. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  7. 2020Improved Code Summarization via a Graph Neural Networkcited by 79
  8. 2020Graph Neural Networks in TensorFlow and Keras with Spektralcited by 59
  9. 2020A Survey of Adversarial Learning on Graphscited by 42

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Cited by year

Citing papers per year: 2021: 2; 2022: 3; 2023: 3.

8 papers.

  1. 2021ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Predictioncited by 204
  2. 2023Towards Better Dynamic Graph Learning: New Architecture and Unified Librarycited by 154
  3. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  4. 2021DIG: A Turnkey Library for Diving into Graph Deep Learning Researchcited by 76
  5. 2022Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networkscited by 24
  6. 2022Spectral Adversarial Training for Robust Graph Neural Networkcited by 12
  7. 20233D-Mol: A Novel Contrastive Learning Framework for Molecular Property Prediction with 3D Informationcited by 6
  8. 2023Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphscited by 2