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README.md

An introduction to graph analysis and modeling

This repository regroups the material (slides, practicals, projects) associated to the course about “graph analysis and modeling”, as a part of the MSc in Statistics for Smart Data.

Schedule (tentative)

Descriptive Analysis of Network Data

November the 6th, 2018

  • Course Statistics on network data, Graph Partitionning - slides
  • Tutorial Basical graph manipulation and Spectral Clustering sheet

Statistical Models for Networks Data: SBM part 1

November the 15th, 2018

  • Course: Mixture Models, EM algorithm - slides
  • Tutorial: Reminder on mixture models sheet

Statistical Models for Networks Data: SBM part 2

  • Course: Variational EM algorithm, Stochastic Block Model - slides
  • Tutorial: Stochastic Block Model and variational inference sheet

November the 22th, 2018

Computer requirements

You need to have a recent version of Rstudio installed with R >= 3.5.1 and the following packages installed:

Basic packages for R extensions

install.packages("devtools")
install.packages("knitr")
install.packages("rmarkdown")
install.packages("aricode")
install.packages("Matrix")

Packages for graph manipulation

install.packages("igraph")
install.packages("sna")
install.packages("network")

Packages for stochastic block models

install.packages("blockmodels")
install.packages("mixer") ## you must install from source

Packages for fancy plotting

install.packages("tidyverse")
install.packages("ggraph")

Evaluation and Projects: extension of the stochastic block model

Subjects of the projects will be discussed on the 22th of November.

Evaluation of the module will be made based on 1) a report (less than 10 pages in English) and 2) A 15 talks presenting your project and 3) the reports sent at the end of each tutorial.

References

Some book (not freely available, sorry)

Some material online

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Material for course about statistical analysis and modeling of networks

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