Français Anglais
Accueil Annuaire Plan du site
Home > Research results > Dissertations & habilitations
Research results
Ph.D de

Ph.D
Group : Graphs, ALgorithms and Combinatorics

Combinatorial Algorithms and Optimization

Starts on 01/12/2014
Advisor : DEZA, Antoine
[COHEN Johanne]

Funding : Bourse association
Affiliation : Université Paris-Saclay
Laboratory : LRI - GALaC

Defended on 15/11/2017, committee :
Co-directrice de thèse :
- Johanne Cohen, Paris-Sud;
Co-directeur de thèse
- Antoine Deza,Paris-Sud.

Raporteurs :
- Ralf Klasing, Bordeaux;
- Dmitri Pasechnik, Oxford;
- Lionel Pournin, Paris XIII.

Examinateurs :
- Ioan Todinca, Orléans;
- Michele Sebag, Paris-Sud;
- Cristina Bazgan, Université Paris-Dauphine.

Research activities :

Abstract :
We investigate three main questions in this thesis. The rst two are related to
graph algorithmic problems. Given general or restricted classes of graphs, we design
algorithms in order to achieve some given result.
We start by introducing the class of k-degenerate graphs which are often used to
model sparse real world graphs. We then focus on enumeration questions for these
graphs. That is, we try and provide algorithms which must output, without duplication,
all the occurrences of some input subgraph with some given properties. In
the scope of this thesis, we investigate the questions of nding all subgraphs which
have the property to be cycles of some given size and all subgraphs which have the
property to be maximal cliques in the input sparse graph. Our two main contributions
related to these problems are a worst-case output size optimal algorithm for
xed-size cycle enumeration and an output sensitive algorithm for maximal clique
enumeration for this restricted class of graphs.
The second main object that we study is also related to graph algorithmic questions,
although in a very dierent setup. We want to consider graphs in a distributed
manner. Each vertex or node has some computing power and can communicate with
its neighbors. Nodes must then cooperate in order to solve a global problem. In
this context, we mainly investigate questions related to nding matchings (a set of
edges of the graph with no common end vertices) assuming any possible initialization
(correct or incorrect) of the system. These algorithms are often referred to as
self-stabilizing since no assumption is made on the initial state of the system. In
this context, our two main contributions are the rst polynomial time self-stabilizing
algorithm returning a 2=3-approximation of the maximum matching and a new selfstabilizing
algorithm for maximal matching when communication is restricted in
such a way as to simulate the message passing paradigm.
Our third object of study is not related to graph algorithms, although some
classical techniques are borrowed from that eld to achieve some of our results.
We introduce and investigate some special families of polytopes, namely primitive
zonotopes, which can be described as the Minkowski sum of short primitive vectors.
We prove some of their combinatorial properties and highlight connections with the
largest possible diameter of the convex hull of a set of points in dimension d whose
coordinates are integers between 0 and k. Our main contributions are new lower
bounds for this diameter question as well as descriptions of small instances of these
polytopes.

Ph.D. dissertations & Faculty habilitations
CAUSAL LEARNING FOR DIAGNOSTIC SUPPORT


CAUSAL UNCERTAINTY QUANTIFICATION UNDER PARTIAL KNOWLEDGE AND LOW DATA REGIMES


MICRO VISUALIZATIONS: DESIGN AND ANALYSIS OF VISUALIZATIONS FOR SMALL DISPLAY SPACES
The topic of this habilitation is the study of very small data visualizations, micro visualizations, in display contexts that can only dedicate minimal rendering space for data representations. For several years, together with my collaborators, I have been studying human perception, interaction, and analysis with micro visualizations in multiple contexts. In this document I bring together three of my research streams related to micro visualizations: data glyphs, where my joint research focused on studying the perception of small-multiple micro visualizations, word-scale visualizations, where my joint research focused on small visualizations embedded in text-documents, and small mobile data visualizations for smartwatches or fitness trackers. I consider these types of small visualizations together under the umbrella term ``micro visualizations.'' Micro visualizations are useful in multiple visualization contexts and I have been working towards a better understanding of the complexities involved in designing and using micro visualizations. Here, I define the term micro visualization, summarize my own and other past research and design guidelines and outline several design spaces for different types of micro visualizations based on some of the work I was involved in since my PhD.