********* 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