Neural‑Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding

Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years. Existing works have demonstrated that vertex representation learned through an embedding method provides superior performance in many real-world applications, such as node classification, link prediction, and community detection. However, most of the existing methods for network embedding only utilize topological information of a vertex, ignoring a rich set of nodal attributes (such as user profiles of an online social network, or textual contents of a citation network), which is abundant in all real-life networks. A joint network embedding that takes into account both attributional and relational information entails a complete network information and could further enrich the learned vector representations. In this work, we present Neural-Brane, a novel Neural Bayesian Personalized Ranking based Attributed Network Embedding. For a given network, Neural-Brane extracts latent feature representation of its vertices using a designed neural network model that unifies network topological information and nodal attributes. Besides, it utilizes Bayesian personalized ranking objective, which exploits the proximity ordering between a similar node pair and a dissimilar node pair. We evaluate the quality of vertex embedding produced by Neural-Brane by solving the node classification and clustering tasks on four real-world datasets. Experimental results demonstrate the superiority of our proposed method over the state-of-the-art existing methods

The French research system : which evolution and which borders ?

We analyse the French Research System with the study of the contracts between the CNRS (Centre National de la Recherche Scientifique) and the companies and test the hypothesis of small world in science. Our working material is the data base of the contracts of the units of the CNRS with economic partners, which has been collecting information since 1986 to 2006. This first application of Network methods and tools to the CNRS contracts allows us to obtain some results: at first, the major firms’s scientific network is not "scale-free" as if competition and strategy between the most large firms dominate the behaviour in R&D investments and management of contracts with public research units. However, in second part, we demonstrate that every discipline network is a "small world", i.e. , that it exists several scientific communities in which the diffusion of information is free and easy, even if its forwards through any actors (some labs or some firms). Probably, there are several "small worlds" in this database as in the scientific collaboration networks. Is seems that the industrial research does not disturb too much the properties of scientific network, as it’s well known in the literature of Sciences Studies