Resources

Introduction to related coding resources.

A number of open-source datasets and tools often used by network applications:

Graph data repositories

  • Open Graph Benchmark (OGB): a collection of realistic, large-scale, and diverse benchmark datasets for machine learning on graphs 1
  • The Network Repository: contains hundreds of real-world networks and benchmark datasets 2
  • Stanford Large Network Dataset Collection (SNAP): analysis of large social and information networks 3
  • OSMnx: a Python package to retrieve, model, analyze, and visualize street networks from OpenStreetMap 4

Graph learning

  • PyTorch Geometric: a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) 5
  • Deep Graph Library (DGL): a Python package for deep learning on graphs 6,
  • Dive into Graphs (DIG): a turnkey library for graph deep learning research 7
  • PyTorch Geometric Temporal: a temporal graph neural network extension library for PyTorch Geometric 8
  • Torch-points3d: a framework for running common deep learning models for point cloud analysis tasks against classic benchmark 9
  • GraphChallenge.org 10
  • XGI: modeling and analyzing complex systems with group (higher-order) interactions11
  • PyGSP: Graph Signal Processing in Python 12
  • NetworkX: a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks 13

Diffusion model

  • NDlib: a Python software package that allows to describe, simulate, and study diffusion processes on complex networks 14
  • Cosasi: a Python package for graph diffusion source localization15
  • EpiModel: Mathematical Modeling of Infectious Disease Dynamics 16

  1. OGB ↩︎

  2. NetworkRepository ↩︎

  3. SNAP ↩︎

  4. OSMnx ↩︎

  5. PyG ↩︎

  6. DGL ↩︎

  7. DIG ↩︎

  8. PyTorch Geometric Temporal ↩︎

  9. Torch Point 3D ↩︎

  10. GraphChallenge ↩︎

  11. XGI ↩︎

  12. PyGSP ↩︎

  13. NetworkX ↩︎

  14. NDLib ↩︎

  15. Cosasi ↩︎

  16. EpiModel ↩︎

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Last updated on Jun 14, 2023