Section: Partnerships and Cooperations

International Initiatives

Visits of International Scientists

• Thi Ha Duong Phan, Academy of Science and Technology in Vietnam, was in visit in the D-NET team for one month in June 2011.

• Renaud Lambiotte, University of Namur in Belgium, was in visit in the D-NET team in November 2011.

• Mariano Beiro, Universidad de Buenos Aires, was in visit in the DNET team for 4 months.

• Duc Thinh Nguyen, Intitut de la Francophonie pour l'Informatique in Hanoï (Vietnam), made his Master internship in the D-NET team for six months from March to September 2011.

Participation In International Programs

STIC AMSUD

Project 09STIC04, Dynamics of Layered Complex Networks, between the LNCC in Brazil (Prime Investigator is Artur Ziviani), UFMG in Brazil, Universidad de Buenos Aires in Argentina, UPMC in France and INRIA. The goal is to develop a better understanding of the issues involved in dealing with dynamic graphs and their applicability to real-world complex networks. We also establish a thematic and collaborative research network between the partners of this project involving complementary backgrounds to deal with the challenges of investigating complex network systems.

Inria/FAPERJ

Project CoDyN (Complex Dynamic Networks) between LNCC and DNET/INRIA. The main goal of the CoDyN project is to lay solid foundations to the characterization of dynamically evolving networks, and to the field of dynamical processes occurring on large scale dynamic interaction networks.

PICS CNRS – Combinatorial Structures for Complex Network Modeling

Participant : Christophe Crespelle.

D-NET is a member of a PICS project of the CNRS between the Academy of Science and Technology in Vietnam and the Laboratoire d'Informatique de Paris 6 (LIP6) and Université Claude Bernard Lyon 1 in France. The project started on january 2010 and will end in december 2012. Its goal is to design models of complex networks that are able to capture at the same time two of their most relevant properties : their heterogeneous degree distribution and their high local density. The goal is to provide very general models that do not make stronger assumptions on the structure of the graphs to be modeled. Our approach is based on the overlapping structure of cliques in complex networks and uses mainly tools coming from combinatorics, graph theory and statistics.