J'ai le plaisir de vous inviter à la soutenance de ma thèse intitulée
"Deciding on the type of the degree distribution of a graph (network)
from traceroute-like measurements".
Cette soutenance aura lieu le 13 décembre 2011 à 10h00 au Laboratoire
d'Informatique de Paris 6 (LIP6) à Jussieu
(http://www.lip6.fr/informations/comment.php), couloir 25-26, salle 105.
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JURY
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Rapporteur:
Mme. Delporte-Gallet Carole Professeur, Université de Paris 7-Denis Diderot
M. Hwang Hsien-kuei Senior Researcher, Institute of Statistical
Science Academia Sinica
Examineur:
M. Crespelle Christophe Maître de conférences, Université Claude
Bernard Lyon 1
M. Latapy Matthieu Directeur de recherche (co-directeur), CNRS
affecté au LIP6
Mme. Legrand Bénédicte Maître de conférences (HDR), Université
Pierre et Marie Curie, LIP6
Mme. Soria Michèle Professeur (directrice), Université Pierre et
Marie Curie, LIP6
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Résumé
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The degree distribution of the Internet topology is considered as one
of its main properties. However, it is only known through a measure-
ment procedure which gives a biased estimate. This measurement may
in first approximation be modeled by a BFS (Breadth-First Search)
tree. We explore here our ability to infer the type (Poisson or power-
law) of the degree distribution from such a limited knowledge. We
design procedures which estimate the degree distribution of a graph
from a BFS or multi-BFS trees, and show experimentally (on models
and real-world data) that our approaches succeed in making the diffe-
rence between Poisson and power-law degree distribution and in some
cases can also estimate the number of links. In addition, we establish
a method, which is a diminishing urn, to analyze the procedure of the
queue. We analyze the profile of the BFS tree from a random graph
with a given degree distribution. The expected number of nodes and
the expected number of invisible links at each level of BFS tree are
two main results that we obtain. Using these informations, we propose
two new methodologies to decide on the type of the underlying graph.
Cordialement,
Xiaomin
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