*********
Tutorials
*********
.. toctree::
:hidden:
Reference Documentation
.. _models_tutorials:
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.
.. toctree::
:maxdepth: 1
:titlesonly:
models/models
.. _simulations_tutorials:
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).
.. toctree::
:maxdepth: 1
:titlesonly:
simulations/erdos_renyi
simulations/sbm
simulations/mmsbm
simulations/rdpg
simulations/corr
simulations/rdpg_corr
.. _cluster_tutorials:
Clustering
==========
The following tutorials explain how to cluster vertex or graph embeddings with two
clustering algorithms, as well as the advantages of these to comparable implementations.
.. toctree::
:maxdepth: 1
:titlesonly:
clustering/autogmm
clustering/kclust
.. _embed_tutorials:
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.
.. toctree::
:maxdepth: 1
:titlesonly:
embedding/AdjacencySpectralEmbed
embedding/OutOfSampleEmbed
embedding/CovariateAssistedEmbed
embedding/MASE
embedding/Omnibus
.. _inference_tutorials:
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.
.. toctree::
:maxdepth: 1
:titlesonly:
inference/latent_position_test
inference/latent_distribution_test
.. _plot_tutorials:
Plotting
========
The following tutorials present ways to visualize the graphs, such as its adjacency matrix, and graph embeddings.
.. toctree::
:maxdepth: 1
:titlesonly:
plotting/heatmaps
plotting/gridplot
plotting/pairplot
plotting/matrixplot
plotting/pairplot_with_gmm
plotting/networkplot
.. _matching_tutorials:
Matching
========
The following tutorials demonstrate how to use the graph matching functionality,
including an introduction to the module, and how to utilize the seeding feature.
.. toctree::
:maxdepth: 1
:titlesonly:
matching/faq
matching/sgm
matching/padded_gm
.. _subgraph_tutorials:
Subgraph
========
The following tutorial demonstrates how to estimate the signal-subgraph of samples of a graph/class model according to either the coherent or incoherent estimator models.
.. toctree::
:maxdepth: 1
:titlesonly:
subgraph/subgraph
.. _vertex_nomination_tutorials:
Vertex Nomination
=================
The following tutorials demonstrate how to use unattributed single graph spectral vertex nomination or vertex nomination via seeded graph matching to find vertices that are related to a given vertex / set of vertices of interest.
.. toctree::
:maxdepth: 1
:titlesonly:
vertex_nomination/SpectralVertexNomination
nominate/vertex_nomination_via_SGM
.. _aligning_tutorials:
Aligning
========
The following tutorials shows how to align two seperate datasets with each other, for better comparison of the data.
.. toctree::
:maxdepth: 1
:titlesonly:
aligning/aligning
.. _connectomics_tutorials:
Connectomics
============
The following tutorials demonstrate how to apply methods in this package to the analysis of connectomics datasets.
.. toctree::
:maxdepth: 1
:titlesonly:
connectomics/mcc