# Tutorial¶

## Simulations¶

The following tutorials present several random graph models, such as Erdos-Renyi model, stochastic block model, and random dot product graph (RDPG) model. These models provide a basis for studying random graphs.

## 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.