Information content: assessing meso-scale structures in complex networks

We propose a novel measure to assess the presence of meso-scale structures in complex networks. This measure is based on the identification of regular patterns in the adjacency matrix of the network, and on the calculation of the quantity of information lost when pairs of nodes are iteratively merged. We show how this measure is able to quantify several meso-scale structures, like the presence of modularity, bipartite and core-periphery configurations, or motifs. Results corresponding to a large set of real networks are used to validate its ability to detect non-trivial topological patterns.Comment: Published as: M. Zanin, P. A. Sousa and E. Menasalvas, Information content: assessing meso-scale structures in complex networks EPL 106 (3), (2014) 3000

Estudio del impacto socio-económico de la Escuela Politècnica Superior de Gandía en la comarca de la Safor

El objetivo principal de este proyecto es conocer el impacto socio-económico que tiene el Campus de Gandía de la Universitat Politècnica de Valencia en la comarca de la Safor como centro receptor de estudiantes, principalmente de la Comunidad Valenciana. Para ello, se analizarán los datos que los estudiantes proporcionarán a través de una encuesta, donde se recogerán datos de interés para el estudio tales como el lugar y tipo de alojamiento durante el curso; la residencia familiar; duración de la estancia; alternativas al Campus de Gandía y los gastos que puedan tener los estudiantes en diferentes partidas como son el alquiler, alimentación o material escolar. Analizados los datos, se espera obtener el gasto medio de los estudiantes y concretamente aquel que se produce en la comarca de la Safor. Junto con este resultado, se espera también conocer el gasto de los visitantes a estos estudiantes.The main purpose of this project is to understand the socio-economic impact that the Gandía Campus of the Polytechnic University of Valencia has on the Safor region as a receiving centre of students, primarily from the Valencian Community. To do so, the personal data that students provide through a survey will be analysed. In the survey, we will collect relevant information for the study, such as the place and type of accommodation during the academic year, the family residence, the length of stay, alternatives to the Gandia Campus, and the expenses that students could have such as rent, food or school supplies. Once this information is analysed, we expect to know the students’ average expenses, specifically those in the Safor region. With that result, we also expect to know the expenses of the visitors who are visiting these students.Muñoz Menasalvas, J. (2019). Estudio del impacto socio-económico de la Escuela Politècnica Superior de Gandía en la comarca de la Safor. Universitat Politècnica de València. http://hdl.handle.net/10251/134478TFG

Tracking recurrent concepts using context

The problem of recurring concepts in data stream classification is a special case of concept drift where concepts may reappear. Although several existing methods are able to learn in the presence of concept drift, few consider contextual information when tracking recurring concepts. Nevertheless, in many real-world scenarios context information is available and can be exploited to improve existing approaches in the detection or even anticipation of recurring concepts. In this work, we propose the extension of existing approaches to deal with the problem of recurring concepts by reusing previously learned decision models in situations where concepts reappear. The different underlying concepts are identified using an existing drift detection method, based on the error-rate of the learning process. A method to associate context information and learned decision models is proposed to improve the adaptation to recurring concepts. The method also addresses the challenge of retrieving the most appropriate concept for a particular context. Finally, to deal with situations of memory scarcity, an intelligent strategy to discard models is proposed. The experiments conducted so far, using synthetic and real datasets, show promising results and make it possible to analyze the trade-off between the accuracy gains and the learned models storage cost

Collaborative data stream mining in ubiquitous environments using dynamic classifier selection

In ubiquitous data stream mining applications, different devices often aim to learn concepts that are similar to some extent. In these applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model achieves stability and accuracy in a variety of situations using both synthetic and real world datasets