# Tutorial¶

## Models¶

This tutorial presents several random graph models: the Erdos-Renyi (ER) model, degree-corrected ER model, stochastic block model (SBM), degree-corrected SBM, and random dot product graph model. These models provide a basis for studying random graphs. All models are shown fit to the same dataset.

## Simulations¶

The following tutorials demonstrate how to easily sample random graphs from graph models such as the Erdos-Renyi model, stochastic block model, and random dot product graph (RDPG).

## Embedding¶

Inference on random graphs depends on low-dimensional Euclidean representation of the vertices of graphs, known as *graph embeddings*, typically given by spectral decompositions of adjacency or Laplacian matrices. Below are tutorials for computing graph embeddings of single graph and multiple graphs.

## Inference¶

Statistical testing on graphs requires specialized methodology in order to account for the fact that the edges and nodes of a graph are dependent on one another. Below are tutorials for robust statistical hypothesis testing on multiple graphs.

## Plotting¶

The following tutorials present ways to visualize the graphs, such as its adjacency matrix, and graph embeddings.

## Matching¶

The following is a brief tutorials how to use the graph matching algorithm, the Fast Approximate Quadratic assignment algorithm..