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PhD graduated
Team : ComplexNetworks
Departure date : 12/31/2020

Supervision : Clémence MAGNIEN

Co-supervision : TARISSAN Fabien

Analyse et modélisation de la diversité des structures relationnelles à l’aide de graphes multipartis

There is no longer any need to prove that digital technology, the Internet and the web have led to a revolution, particularly in the way people get information. Like any revolution, it is followed by a series of issues : equal treatment of users and suppliers, ecologically sustainable consumption, freedom of expression and censorship, etc. Research needs to provide a clear vision of these stakes.
Among these issues, we can talk about two phenomena : the echo chamber phenomenon and the filter bubble phenomenon. These two phenomena are linked to the lack of diversity of information visible on the Internet, and one may wonder about the impact of recommendation algorithms. Even if this is our primary motivation, we are moving away from this subject to propose a general scientific framework to analyze diversity. We find that the graph formalism is useful enough to be able to represent relational data. More precisely, we will analyze relational data with entities of different natures. This is why we chose the n-part graph formalism because this is a good way to represent a great diversity of data. Even if the first data we studied is related to recommendation algorithms (music consumption or purchase of articles on a platform) we will see over the course of the manuscript how this formalism can be adapted to other types of data (politicized users on Twitter, guests of television shows, establishment of NGOs in different States ...). There are several objectives in this study :

  • Mathematically define diversity indicators on the n-part graphs.
  • Algorithmically define how to calculate them.
  • Program these algorithms to make them a usable computer object.
  • Use these programs on quite varied data.
  • See the different meanings that our indicators can have.
We will begin by describing the mathematical formalism necessary for our study. Then we will apply our mathematical object to basic examples to see all the possibilities that our object offers us. This will show us the importance of normalizing our indicators, and will motivate us to study random normalization. Then we will see another series of examples which will allow us to go further on our indicators, going beyond the static and tripartite side to approach graphs with more layers and depending on time. To be able to have a better vision of what the real data brings us, we will study our indicators on completely randomly generated graphs.

Defence : 12/04/2020 - 11h - Zoom -

Jury members :

Mme ROBARDET Céline (Professeure au LIRIS), [Rapporteure]
M ABDESSALEM Talel (Professeur à l'INFRES), [Rapporteur]
Mme LUENGO Vanda (Professeure au Lip6), Examinatrice
M CAZABET Rémy (Maître de conférence au LIRIS)
M HERVE Nicolas (Chercheur à l'INA)
M. BENBOUZID Bilel (Maître de conférence au LISIS)
Mme MAGNIEN Clémence (Directrice de Recherche au Lip6)
M TARISSAN Fabien (Chercheur CNRS à l'ENS Paris Saclay)

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