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https://github.com/jchiquet/coursestatnetwork
Material for course about statistical analysis and modeling of networks
https://github.com/jchiquet/coursestatnetwork
graph network-analysis stochastic-block-model
Last synced: about 1 month ago
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Material for course about statistical analysis and modeling of networks
- Host: GitHub
- URL: https://github.com/jchiquet/coursestatnetwork
- Owner: jchiquet
- License: gpl-3.0
- Created: 2018-05-17T11:41:25.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-10-11T14:22:47.000Z (about 5 years ago)
- Last Synced: 2023-03-05T01:43:41.757Z (almost 2 years ago)
- Topics: graph, network-analysis, stochastic-block-model
- Language: TeX
- Size: 10.6 MB
- Stars: 1
- Watchers: 1
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
title: "An introduction to graph analysis and modeling"
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
> 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](http://www.ensai.fr/formation/msc-in-statistics-for-smart-data.html).
## Schedule (tentative)
### Descriptive Analysis of Network Data
November the 6th, 2018
- *Course* Statistics on network data, Graph Partitionning - [slides](https://github.com/jchiquet/CourseStatNetwork/raw/master/slides/DescriptiveAnalysis/DescriptiveAnalysis.pdf)
- *Tutorial* Basical graph manipulation and Spectral Clustering [sheet](https://github.com/jchiquet/CourseStatNetwork/raw/master/practicals/DescriptiveAnalysis/tuto_DescriptiveAnalysis.pdf)
### Statistical Models for Networks Data: SBM part 1
November the 15th, 2018
- *Course*: Mixture Models, EM algorithm - [slides](https://github.com/jchiquet/CourseStatNetwork/raw/master/slides/GraphModel/GraphModels.pdf)
- *Tutorial*: Reminder on mixture models [sheet](https://github.com/jchiquet/CourseStatNetwork/raw/master/practicals/MixtureModelsEM/tuto_mixtureModelsEM.pdf)
### Statistical Models for Networks Data: SBM part 2
- *Course*: Variational EM algorithm, Stochastic Block Model - [slides](https://github.com/jchiquet/CourseStatNetwork/raw/master/slides/GraphModel/GraphModels.pdf)
- *Tutorial*: Stochastic Block Model and variational inference [sheet](https://github.com/jchiquet/CourseStatNetwork/raw/master/practicals/GraphModels/tuto_GraphModels.pdf)
November the 22th, 2018
## Computer requirements
You need to have a recent version of [Rstudio](https://www.rstudio.com/products/rstudio/download/) installed with [R](https://cran.r-project.org) >= 3.5.1 and the following packages installed:
### Basic packages for R extensions
```{r other packages, eval = FALSE}
install.packages("devtools")
install.packages("knitr")
install.packages("rmarkdown")
install.packages("aricode")
install.packages("Matrix")
```
### Packages for graph manipulation
```{r graph packages, eval = FALSE}
install.packages("igraph")
install.packages("sna")
install.packages("network")
```
### Packages for stochastic block models
```{r SBM packages, eval = FALSE}
install.packages("blockmodels")
install.packages("mixer") ## you must install from source
```
### Packages for fancy plotting
```{r tidy packages, eval = FALSE}
install.packages("tidyverse")
install.packages("ggraph")
```
## Evaluation and Projects: extension of the stochastic block model
- *Projects are here*: [subjects](https://github.com/jchiquet/CourseStatNetwork/raw/master/projects/projects.pdf)
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
* [Rstudio cheat sheets](https://www.rstudio.com/resources/cheatsheets/)
Some book (not freely available, sorry)
* [Statistical Analysis of Network Data: Methods and Models, by Eric D. Kolaczyk](https://books.google.fr/books?id=Q-GNLsqq7QwC&source=gbs_book_similarbooks)
* [Statistical Analysis of Network Data with R, by Eric D. Kolaczyk, Gábor Csárdi](https://books.google.fr/books?id=cNMhBAAAQBAJ&source=gbs_navlinks_s)
* Bishop, C. (2000). Introduction to graphical modelling, 2nd edn. Springer, New York.
* Højsgaard, S., Edwards , D., Lauritzen, S. (2012). Graphical Models with R. Springer, New York.
Some material online
* [Eric D. Kolazcyk's course slides](http://math.bu.edu/ness12/ness2012-shortcourse-kolaczyk.pdf)
* [Catherine Matias's course page (in French)](http://cmatias.perso.math.cnrs.fr/Cours_Graphes.html)