index.Rmd

---
title: "Probabilistic models for networks"
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bibliography: TP_SBM/references.bib
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---
<img src="img/sophie_donnet_profil2022.png" alt="Ecological network" width = "240px" align="center"> </img>

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You will find here all the material I use for my 3 hours  class on **probabilistic models for network analysis**. 



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<img src="img/NetworkImage.png" alt="Ecological network" width = "240px" align="right"> </img>

 



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### **Introduction** 

Ecological networks are made up of nodes, representing biological entities of interest and edges representing the interaction being studied.  Stochastic block models (SBMs) and their extension to bipartite networks are convenient tools to modelize heterogeneity in (ecological) networks by introducing blocks of nodes sharing the same pattern of connection.  This short-course presents SBMs for unipartite, bipartite or more complex networks and illustrates their flexibility.


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### **Material for the theoretical part** 


Here are the slides of the course

  - The first part is an **Introduction to block models** [Part 1](Slides_SBM_LBM/part1_SMB_LBM_donnet.pdf)
  - The second part will adress  the **Modelling of a collection of networks** [Part 2](Slides_Col_SBM/part3_ColSBM.pdf). This part is based on  @chabert2022,  available on [arxiv](https://arxiv.org/pdf/2206.00560.pdf).   This second part will be dealt with only if we have time.

 

Additional material on Stochastic Block Models can be found the book Chapter 6 *Using Latent Block Models to Detect Structure in Ecological Networks*  in @AubertChap6. 

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### **R-tutorial**

This class will include an R-tutorial session available [here](TP_SBM/TutorialBM.html). 
You can download the `TutorialBM.qmd` file [here](TP_SBM/TutorialBM.qmd)

It is mainly based on the  R-package [`sbm`](https://cran.r-project.org/web/packages/sbm/index.html).   Please find informations [here](https://grosssbm.github.io/sbm/). 

Some additional R packages are needed. 
  
```{r, eval  = FALSE, echo = TRUE }
install.packages("sbm")
install.packages("GGally") # To plot networks
install.packages('network') 
install.packages('RColorBrewer') # to have nice colors
install.packages('knitr') # to plot nice tables
```
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Note that a shiny application is also proposed. You should install the last version on your machine

```{r install shiny, eval = FALSE}
remotes::install_github("Jo-Theo/shinySbm")
shinySbm::run_app()
``` 

It can also be used online [here](https://shiny.migale.inrae.fr/app/ShinySBM) (not always the latest version).  


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In case we have anough time, the second part of the tutorial will deal with collection of networks and we  will  use the R package `colSBM` available on [Github](https://rdrr.io/github/Chabert-Liddell/colSBM/f/README.Rmd). 

```{r install colSBM, eval = FALSE}
remotes::install_github("Chabert-Liddell/colSBM")
``` 

The tutorial on `colSBM`  is available at [this link](https://chabert-liddell.github.io/colSBM/articles/tutorial.html)   
 
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### **Data sets** 

 - The first part of the  tutorial will rely on the fungus-tree interaction network studied by @tree_fungus_network, available with the package `sbm`.  
 - We may also analyze the multipartite ecological network collected by @Dattilo (also available in `sbm`)
 - Finally, we will maybe use the dataset of @thompson2003impacts provided in the `colSBM` package
      

```{r data , eval = FALSE}
library(sbm)
data("fungusTreeNetwork")
data("multipartiteEcologicalNetwork")
library(colSBM)
data("foodwebs")

``` 
 
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### **References**