The effect of discrete vs. continuous-valued ratings on reputation and ranking systems

When users rate objects, a sophisticated algorithm that takes into account ability or reputation may produce a fairer or more accurate aggregation of ratings than the straightforward arithmetic average. Recently a number of authors have proposed different co-determination algorithms where estimates of user and object reputation are refined iteratively together, permitting accurate measures of both to be derived directly from the rating data. However, simulations demonstrating these methods' efficacy assumed a continuum of rating values, consistent with typical physical modelling practice, whereas in most actual rating systems only a limited range of discrete values (such as a 5-star system) is employed. We perform a comparative test of several co-determination algorithms with different scales of discrete ratings and show that this seemingly minor modification in fact has a significant impact on algorithms' performance. Paradoxically, where rating resolution is low, increased noise in users' ratings may even improve the overall performance of the system.Comment: 6 pages, 2 figure

Detecting modules in dense weighted networks with the Potts method

We address the problem of multiresolution module detection in dense weighted networks, where the modular structure is encoded in the weights rather than topology. We discuss a weighted version of the q-state Potts method, which was originally introduced by Reichardt and Bornholdt. This weighted method can be directly applied to dense networks. We discuss the dependence of the resolution of the method on its tuning parameter and network properties, using sparse and dense weighted networks with built-in modules as example cases. Finally, we apply the method to data on stock price correlations, and show that the resulting modules correspond well to known structural properties of this correlation network.Comment: 14 pages, 6 figures. v2: 1 figure added, 1 reference added, minor changes. v3: 3 references added, minor change

Mass Media Influence Spreading in Social Networks with Community Structure

We study an extension of Axelrod's model for social influence, in which cultural drift is represented as random perturbations, while mass media are introduced by means of an external field. In this scenario, we investigate how the modular structure of social networks affects the propagation of mass media messages across the society. The community structure of social networks is represented by coupled random networks, in which two random graphs are connected by intercommunity links. Considering inhomogeneous mass media fields, we study the conditions for successful message spreading and find a novel phase diagram in the multidimensional parameter space. These findings show that social modularity effects are of paramount importance in order to design successful, cost-effective advertising campaigns.Comment: 21 pages, 9 figures. To appear in JSTA

Contribution à l'étude électrochimique des composés iodés de contraste :application à l'analyse pharmaceutique

Doctorat en sciences pharmaceutiquesinfo:eu-repo/semantics/nonPublishe

Contribution à l'étude électrochimique des composés iodés de contraste :application à l'analyse pharmaceutique

Doctorat en sciences pharmaceutiquesinfo:eu-repo/semantics/nonPublishe

Polarographie Impulsiornelle Differentielle De Derives Triiodes De l'Acide Benzoique.

Three triiodo derivatives of benzoic acid have been separated and analyzed by differential pulse polarography in acidic, neutral and alkaline medium. The selectivity of the method, and a comparison of the resolution with conventional polarography, are pointed out. An order of reduction of the atoms is proposed. © 1975, Taylor & Francis Group, LLC. All rights reserved.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Role of second trials in cascades of information over networks.

Contains fulltext : 81645.pdf (publisher's version ) (Open Access)We study the propagation of information in social networks. To do so, we focus on a cascade model where nodes are infected with probability p_{1} after their first contact with the information and with probability p_{2} at all subsequent contacts. The diffusion starts from one random node and leads to a cascade of infection. It is shown that first and subsequent trials play different roles in the propagation and that the size of the cascade depends in a nontrivial way on p_{1} , p_{2} , and on the network structure. Second trials are shown to amplify the propagation in dense parts of the network while first trials are dominant for the exploration of new parts of the network and launching new seeds of infection

Critical study of the interferences of silicic derivatives on fluoride determination

info:eu-repo/semantics/publishe