Modeling and measurements on field graphs

Our work is the in-depth study of graph models, especially of large sizes.

In this context, we have deployed the programming techniques previously acquired in Python with the use of NetworkX as a library.

The project had two main axes: graph theory: everything theoretical (definitions, measures, execution examples...) and applications: recovery of a field graph, comparison between algorithms and comparison between models.

Moreover, this project was an opportunity to master graph theory, Python, NetworkX and especially an enrichment for our Data Mining culture. And we are satisfied to have improved the end of the world algorithm and to have implemented our own version of the community detection algorithm: label propagation, and to have used the Twitter API as a means to generate terrain graphs.