Algorithms for nonuniform networks

In this thesis, observations on structural properties of natural networks are taken as a starting point for developing efficient algorithms for natural instances of different graph problems. The key areas discussed are sampling, clustering, routing, and pattern mining for large, nonuniform graphs. The results include observations on structural effects together with algorithms that aim to reveal structural properties or exploit their presence in solving an interesting graph problem. Traditionally networks were modeled with uniform random graphs, assuming that each vertex was equally important and each edge equally likely to be present. Within the last decade, the approach has drastically changed due to the numerous observations on structural complexity in natural networks, many of which proved the uniform model to be inadequate for some contexts. This quickly lead to various models and measures that aim to characterize topological properties of different kinds of real-world networks also beyond the uniform networks. The goal of this thesis is to utilize such observations in algorithm design, in addition to empowering the process of network analysis. Knowing that a graph exhibits certain characteristics allows for more efficient storage, processing, analysis, and feature extraction. Our emphasis is on local methods that avoid resorting to information of the graph structure that is not relevant to the answer sought. For example, when seeking for the cluster of a single vertex, we compute it without using any global knowledge of the graph, iteratively examining the vicinity of the seed vertex. Similarly we propose methods for sampling and spanning-tree construction according to certain criteria on the outcome without requiring knowledge of the graph as a whole. Our motivation for concentrating on local methods is two-fold: one driving factor is the ever-increasing size of real-world problems, but an equally important fact is the nonuniformity present in many natural graph instances; properties that hold for the entire graph are often lost when only a small subgraph is examined.reviewe

Agrupamiento local en grafos dirigidos

En este trabajo se presenta un método de agrupamiento local en grafos dirigidos: dado un vértice semilla, se determinó el grupo de vértices al que pertenece de tal forma que los vértices seleccionados sean estructuralmente cercanos de la semilla. A un grafo dirigido se le puede asociar una cadena de Markov que corresponde a una caminata aleatoria ciega en el grafo. Se aprovechó esta conexión para expresar cercanía estructural en términos de los tiempos de absorción para detectar vértices que son “cercanos” al vértice semilla. Se detectó el grupo de un vértice a través de caminatas aleatorias cortas repetidas desde el vértice semilla, analizando la frecuencia de visitas a los otros vértices. Se experimentó con grafos pequeños para comparar el resultado los tiempos exactos de absorción. El agrupamiento local puede ser aplicado a diferentes fenómenos reales, por ejemplo, en propagación de epidemias, balanceo de carga, etc

Studying the effects of instance structure in algorithm performance

the amount of resources consumed in the worst case. However, it has become evident that the instance size by itself is an insufficient measure and that the worst-case scenario is often unin-formative in practice. As a complementary analysis, we propose the examination of structural properties present in the instances and the effects they have on algorithm performance; our goal is to characterize complexity in terms of instance structure. We propose a framework for identifying and characterizing hard instances based on algorithm behaviour as well as a case study applying the framework on the graph coloring problem

Assortative and modular networks are shaped by adaptive synchronization processes

Modular organization and degree-degree correlations are ubiquitous in the connectivity structure of biological, technological, and social interacting systems. So far most studies have concentrated on unveiling both features in real world networks, but a model that succeeds in generating them simultaneously is needed. We consider a network of interacting phase oscillators, and an adaptation mechanism for the coupling that promotes the connection strengths between those elements that are dynamically correlated. We show that, under these circumstances, the dynamical organization of the oscillators shapes the topology of the graph in such a way that modularity and assortativity features emerge spontaneously and simultaneously. In turn, we prove that such an emergent structure is associated with an asymptotic arrangement of the collective dynamical state of the network into cluster synchronization

A framework for informing consumers on the ecological impact of products at point of sale

The use of intelligent information technologies has the means to provide ecological information just-in-time, thus alleviating consumers' cognitive burden at the time of purchase. We propose a computational framework for supporting consumer awareness of the ecological impact of products they consider purchasing at point of sale. The proposed framework permits consulting multiple information sources through diverse access interfaces, combined with a recommendation engine to score product greenness. We evaluate our approach in terms of usability, performance, and user-influence tests through two conceptual prototypes: an online store and an augmented reality interface to use at physical stores. Our findings suggest that providing ecological information at the time of purchase is able to direct consumers' preference towards products that are ecological and away from products that are not; consumers also express willingness to pay slightly more for ecological products. The experimental results obtained with the interface prototypes are statistically significant

Gene Copy Number Quantification of SHOX , VAMP7 , and SRY for the Detection of Sex Chromosome Aneuploidies in Neonates

Aims: To explore the feasibility of detecting sex chromosome aneuploidies (SCAs) by means of gene copy number quantification of short stature homeobox (SHOX), vesicle-associated membrane protein 7 (VAMP7), and SRY in newborns. Materials and Methods: Gene doses of SHOX, VAMP7, and SRY were determined by quantitative polymerase chain reaction (qPCR) using DNA obtained from dried blood samples from newborns. Relative quantification values were obtained. An aneuploidy profile was established according to cutoff values. Samples with ≥2 gene doses (out of range) were reanalyzed, and those with aneuploidy profiles were confirmed by karyotyping. Sensitivity, specificity, and positive and negative predictive values were obtained. Results: A total of 10,033 samples were collected (4945 females and 5088 males). Of 244 (2.43%) samples with ≥2 gene doses that were retested, 20 cases were confirmed. The overall incidence of SCAs was 1 in 500 live newborns. There were six cases of Turner syndrome (1/824), 3 cases of XXX (1/1648), 7 cases of Klinefelter syndrome (1/726), and 4 cases of of XYY (1/1272). The sensitivity was 0.952 (95.24%), specificity of 0.975 (97.56%), positive predictive value of 0.909 (90.91%), and negative predictive value of 0.987 (98.77%). Conclusions: Gene copy number analyses of VAMP7, SHOX, and SRY genes by qPCR from blood samples spotted onto filter paper is a highly reliable method for the early detection of male and female SCAs