Egypt 2.0

Blogging as a phenomenon has spread far from its initial western context and provides new interesting research topics on the implications of the blogosphere in more authoritarian states. We however face many problems when trying to connect the technological complexity of the new information era and the social scientific theories of our discipline. By applying a framework designed to bring order and validity to our efforts to connect social science with information technology I try to define key features of both blogging and the blogosphere. From this framework will we move on to charge the key features with a societal implication, in our case will the features be connected with their potential to assemble the blogs into a social platform for political deliberation. And after presentation of the problems the researcher faces when gathering his data will I present the context in which the Egyptian political blogosphere exist. From this I will move on to propose the hypothesis that an authoritarian state actually will strengthen the quality of the information disseminated in the blogosphere. We will eventually end up with an originally gathered data set and discuss what conclusions we dare to draw

Représentation d'un grand réseau à partir d'une classification hiérarchique de ses sommets

International audienceGraph visualization is an important tool to understand the main features of a network but, when the number of nodes in the graph exceeds few hundreds, standard visualization methods are computationally expensive. Moreover, force directed algorithms do not help the understanding of the community structure of the newtork, if is exists. In this paper, a new visualization method based on a hierarchical clustering of the nodes of the graph is proposed. It can handle graphs having several thousands nodes in a few seconds. Several simplified representations of the graph are accessible, giving the user the opportunity to understand the macroscopic organization of the network and then, to focus on some particular parts of the graph. This refining process is controlled as follows. Partitions under consideration are evaluated via the classical modularity quality measure. A distribution of the quality measure in the case of graphs without structure is obtained by applying the proposed method to random graphs with the same degree distribution as the graph under study. Then only significant partitions are shown during the refining process. This approach is illustrated on several public datasets and compared with other visualization methods meant to emphasize the graph communities. It is also tested on a large network built from a corpus of medieval land charters