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Analyse de la structure communautaire des réseaux bipartis

Abstract : In the real world, numerous networks appear naturally, they are everywhere, in many disciplines, for example in computer science with router networks, satellite networks, webpage networks, in biology with neural networks, in ecology with biological interaction networks, in linguistic with synonym networks, in law with legal decision networks, in economy with interbank networks, in social sciences and humanities with social networks. Generally, a network reflects the interactions between many entities of a system. These interactions have different sources, a social link or a friendship link in a social network, a cable in a router network, a chemical reaction in a protein-protein interaction network, a hyperlink in a webpage network. Furthermore, the rapid democratization of digital technology in our societies, with internet in particular, leads to create new systems which can be seen as networks. Finally, all these networks depict very specific features : they come from pratical contexts, most of the time they are big (they may be comprised of several billion of nodes and links, containing a large amount of information), they share statistical properties. In this regard, they are called real-world networks or complex networks. Nowaday, network science is a research area in its own right focusing on describing and modeling these networks in order to reveal their main features and improve our understanding of their mecanisms. Most of the works in this area use graphs formalism which provides a set of mathematical tools well suited for analyzing the topology of these networks. It exists many applications, for instance applications in spread of epidemy or computer viruses, weakness of networks in case of a breakdown, attack resilience, study for link prediction, recommandation, etc. One of the major issue is the identification of community structure. The large majority of real-world networks depicts several levels of organization in their structure. Because of there is a weak global density coupled with a strong local density, we observe that nodes are usually organized into groups, called communities, which are more internally connected than they are to the rest of the network. Moreover, these structures have a meaning in the network itself, for example communities of a social network may correspond to social groups (friends, families, etc.), communities of a protein-protein network may translate fonctions of a cell, communities may be also related to similar subjects in a webpage network [...]
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  • HAL Id : tel-02966420, version 1

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Raphaël Tackx. Analyse de la structure communautaire des réseaux bipartis. Réseaux sociaux et d'information [cs.SI]. Sorbonne Université, 2018. Français. ⟨NNT : 2018SORUS550⟩. ⟨tel-02966420⟩

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