We are interested in all aspects of real world networks and their models, from internet measurements to random graphs, from social network analysis to spreading phenomena, and from graph algorithms to biological networks.
Maximilien Danisch, Jean-Loup Guillaume and Bénédicte Le Grand
CompleNet 2013, Berlin
We propose a framework to unfold the ego-centered community structure of a given node in a network. The framework is not based on the optimization of a quality function, but on the study of the irregularity of the decrease of a similarity measure. It is a practical use of the notion of multi-ego-centered community and we validate the pertinence of the approach on a real-world network of wikipedia pages.
Adrien Friggeri, Jean-Philippe Cointet and Matthieu Latapy
Complex Systems 19, 2011
We designed an experiment to observe a spreading phenomenon in the blogosphere. This experiment relies on a small applet that participants copy on their own web page. We present the obtained dataset, which we freely provide for study, and conduct basic analysis. We conclude that, despite the classical assumption, in this experiment famous blogs do not necessarily act as super spreaders.
Bénédicte Le Grand, Marie-Aude Aufaure et Michel Soto
Conférence EGC 2009 (Extraction et Gestion des Connaissances), Strasbourg, France, 27-30 janvier 2009
In this paper, Formal Concept Analysis and Galois lattices are used for the analysis of complex datasets, online social networks in particular. Lattice-inspired statistics computed on the objects of the lattice provide their « conceptual distribution ». An experimentation conducted on four social networks’ samples shows how these statistics may be used to characterize these networks and filter them automatically.
Pierre Borgnat, Eric Fleury, Jean-Loup Guillaume, Clémence Magnien, Céline Robardet et Antoine Scherrer
Proceedings of NATO Advanced Study Institute on Mining Massive Data Sets for Security, IOS Press, 2008
Most real networks often evolve through time: changes of topology can occur if some nodes and/or edges appear and/or disappear, and the types or weights of nodes and edges can also change even if the topology stays static. Mobile devices with wireless capabilities (mobile phones, laptops, etc.) are a typical example of evolving networks where nodes or users are spread in the environment and connections between users can only occur if they are near each other. This whois- near-whom network evolves every time users move and communication services (such as the spread of any information) will deeply rely on the mobility and on the characteristics of the underlying network. This paper presents some recent results concerning the characterization of the dynamics of complex networks through three different angles: evolution of some parameters on snapshots of the network, parameters describing the evolution itself, and intermediate approaches consisting in the study of specific phenomena or users of interest through time.