# | date | session | topic | material |
---|---|---|---|---|

First part |
Descriptive Analysis of Network Data | |||

1.1 | 19/10 | 3h of course | Statistics on network data, Graph Partitionning | slides |

1.2 | 19/10 | 3h of practical | Basical graph manipulation and Spectral Clustering | sheet 1 |

Second part |
Statistical Models for Networks Data | |||

2.1 | 7/11 | 3h of course | Mixture Models, (variational) EM algorithm, Stochastic Block Model | slides |

2.2 | 7/11 | 3h of practical | Stochastic Block Model and variational inference | sheet 2 |

Third part |
Inference of Network Topology | |||

3.1 | 28/11 | 3h of course | Association networks, Gaussian graphical models | slides |

3.2 | 7/11 | 3h of practical | Sparse inference of Gaussian graphical models | sheet 3 |

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 report sent at the end of each tutorial

Some book (not freely available, sorry)

- Statistical Analysis of Network Data: Methods and Models, by Eric D. Kolaczyk
- Statistical Analysis of Network Data with R, by Eric D. Kolaczyk, Gábor Csárdi
- 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