# 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) interactions^{11}**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 localization^{15}**EpiModel**: Mathematical Modeling of Infectious Disease Dynamics^{16}