XieResearchGroup
  • About the Group
  • HPC Environments
    • Summary of HPCs
      • DGX (Group)
      • DLS (Department)
      • Dragon (Group)
    • HPC User Guide (Must Read)
      • Overall Workflow
      • Connect to HPCs
      • User Directories
      • Run Jobs
      • Data Backup
  • Useful Tutorials
    • Linux Tutorial
      • Linux Commands
    • Docker Tutorial
      • Intro to Docker
      • Intro to NVIDIA Docker
      • Use Docker for Deep Learning
      • Docker Useful Commands
    • Jupyter Notebook Tutorial
      • Run Jupyter Server with GPU Access on HPCs
    • HTCondor Tutorial
      • Introduction of HTCondor
      • Quick Start Guide
      • Submitting Vanilla Job
      • Submitting Docker Job
      • HTCondor Useful Commands
    • Better Deep Learning
      • Better Training
      • Better Generalization
      • Better Prediction
    • About Graph Neural Networks
    • Data Preprocessing
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  • Contribute to the Wiki
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  1. Useful Tutorials

About Graph Neural Networks

For the initial learning about Graph Neural Networks (GNNs), it is better to start with several survey papers: https://github.com/thunlp/GNNPapers#survey-papers.

To follow up on the latest topics in this field, the GNN workshops in top AI conferences are good choices to be tracked: https://github.com/naganandy/graph-based-deep-learning-literature#related-workshops.

For practical implements, two libraries are commonly used in this field: Pytorch Geometric https://github.com/rusty1s/pytorch_geometric and Deep Graph Library https://github.com/dmlc/dgl.

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Last updated 2 years ago

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