Abstract : A complex network is a interaction network of entities where global behavior is not deductible from the individual behaviors of each entities, leading to new properties emergence. Our problem is the network analysis ad modeling. Network analysis needs a formalism to assemble together the structure (static approach) and the function (dynamic approach), and to have a better understanding of the networks caracteristics. First, in this thesis, we introduce common used network modeling based on graph theory, having the role to simulate complex networks. By analyzing weakness of this models about a convincing representation of real networks (social sciences, computers, biology), we bring a formal general definition of a network using pretopology theory, allowing us to have a better reproduction of system dynamics. With that definition comes series of data structures allowing us to develop a whole algorithmic surrounding the model. Secondary, we propose new analyzing algorithms based on element classification and on centers search, giving power tools for decision aid. To finish, we introduce a software library permitting efficient simulations of every models based on pretopology theory.