A novel granular approach for detecting dynamic online communities in social network

The great surge in the research of community discovery in complex network is going on due to its challenging aspects. Dynamicity and overlapping nature are among the common characteristics of these networks which are the main focus of this paper. In this research, we attempt to approximate the granular human-inspired viewpoints of the networks. This is especially helpful when making decisions with partial knowledge. In line with the principle of granular computing, in which precision is avoided, we define the micro- and macrogranules in two levels of nodes and communities, respectively. The proposed algorithm takes microgranules as input and outputs meaningful communities in rough macrocommunity form. For this purpose, the microgranules are drawn toward each other based on a new rough similarity measure defined in this paper. As a result, the structure of communities is revealed and adapted over time, according to the interactions observed in the network, and the number of communities is extracted automatically. The proposed model can deal with both the low and the sharp changes in the network. The algorithm is evaluated in multiple dynamic datasets and the results confirm the superiority of the proposed algorithm in various measures and scenarios

Agglomerative hierarchical clustering algorithm for community detection in large-scale complex networks

Abstract: In this thesis several algorithms are proposed to compute efficiently high quality community structure in large-scale complex networks. First, a novel similarity measure that determines the structural similarity in a graph by dynamically diffusing and capturing information beyond the immediate neighborhood of connected nodes. This new similarity is modeled as an iterated function that can be solved by fixed point iteration in super-linear time and memory complexity, so it is able to analyze large-scale graphs. In order to show the advantages of the proposed similarity in the community detection task, we replace the local structural similarity used in the SCAN algorithm with the proposed similarity measure, improving the quality of the detected community structure and also reducing the sensitivity to the parameter $\epsilon$. Second, a novel fast heuristic algorithm for multi-scale and hierarchical community detection inspired on an agglomerative hierarchical clustering technique. This algorithm uses the Dynamic Structural Similarity in a heuristic agglomerative hierarchical algorithm, that does not merge only clusters with maximal similarity as in the classical hierarchical approach, but merges any cluster that does not meet a community definition passed by parameter with its most similar adjacent clusters. The algorithm computes the similarity between clusters at the same time is checking if each cluster meets the specified community definition. It is done in linear time complexity in terms of the number of cluster in the iteration. Since a complex network is a sparse graph, this approach has a super linear time complexity with respect to the size of the input in the average case scenario, making it suitable to be applied on large-scale complex networks. Third, an efficient algorithm to detect fuzzy and crisp overlapping community structure. This algorithm leverages the disjoint community structure generated by the heuristic algorithm proposed above. Three core elements have been proposed to compute the overlapping community structure: \emph{i)} A connectivity function that quantifies the density of connections of a node towards a disjoint community, that relies its computation on the Dynamic Structural Similarity measure. \emph{ii)} An $\epsilon$-Core community definition that increases the probability of identifying in-between communities in the disjoint community structure. \emph{iii)} A membership function to compute the soft partition from the core disjoint communities. Because this algorithm keeps the same computational complexity of the original disjoint algorithm, it is still applicable to large-scale graphs. Finally, an extensive experimentation is performed in order to test the properties, efficiency and efficacy of the proposed algorithms and to compare them with the state-of-the-art. The experimental results show that the proposed algorithms provide better trade-off among the quality of the detected community structure, computational complexity and usability, compared to the state-of-the-art.En esta tesis se proponen varios algoritmos para computar eficientemente estructura de comunidad de alta calidad en redes complejas de gran escala. Primero, se propone una nueva medida que determina la similitud estructural en un grafo mediante la difusión y captura de información mas allá de la vecindad inmediata de los nodos conectados que están siendo analizados. Esta nueva similitud está modelada como una función iterada que puede ser calculada por iteración a punto fijo en complejidad de tiempo y memoria super-lineal, por lo tanto puede utilizarse para analizar grafos de gran escala. Para mostrar las ventajas de la similitud estructural propuesta, se ha reemplazado la similitud estructural local utilizada en el algoritmo SCAN, con la similitud estructural dinámica, mejorando así la calidad de la estructura de comunidad detectada y también reduciendo la sensibilidad al parámetro $\epsilon$. Segundo, se propone un algoritmo heurístico novedoso para detección de comunidades jerárquicas multi-escala que está inspirado en una técnica de agrupamiento jerárquica aglomerante. Este algoritmo utiliza la similitud estructural dinámica en un algoritmo heurístico jerárquico algomerante, que no une solamente las comunidades con máxima similitud tal como en la técnica jerárquica clásica, sino que une cualquier comunidad que no cumple una definición de comunidad pasada como parametro, con sus comunidades vecinas con las cuales presenta mayor similitud. El algoritmo computa la similitud entre las comunidades a la vez que verifica si cumplen la definición de comunidad pasada como parámetro. Esto es hecho en tiempo lineal en términos del número de comunidades en la iteración. Ya que una red compleja es un grafo disperso, esta aproximación presenta una complejidad de tiempo super-lineal en el caso promedio con respecto al tamaño del grafo entrada, por lo tanto puede ser aplicada en redes complejas de gran escala. Tercero, se propone un algoritmo novedoso para detectar estructura de comunidad superpuesta, tanto difusa como nítida. Este algoritmo utiliza la estructura de comunidad disyunta generada por el algoritmo heurístico propuesto anteriormente. Se proponen tres componentes principales para computar la estructura de comunidad superpuesta. i) Una función de conectividad que cuantifica la densidad de conexiones de un vertice hacia una comunidad disyunta, y su computación está basada en los valores de la similitud estructural dinámica. ii) Una definición de comunidad llamada Comunidad $\epsilon$-Central que incrementa la probabilidad de detectar comunidades superpuestas preliminares en la estructura de comunidad disyunta. iii) Una función de probabilidad que computa la estructura de comunidad difusa a partir de la estructura de comunidad disyunta. Ya que este algoritmo presenta la misma complejidad computacional que el algoritmo original, entonces sigue siendo aplicable a redes complejas de gran escala. Finalmente, una experimentación extensiva ha sido desarrollada con el fin de probar las propiedades, eficacia y eficiencia de los algoritmos propuestos, y para compararlos con el estado del arte. Los resultados experimentales muestran que los algoritmos propuestos proveen un mejor balance entre calidad de la estructura de comunidad detectada, eficiencia de computación y facilidad de uso, comparados con el estado del arte.Maestrí

Circuits and Systems for Lateral Flow Immunoassay Biosensors at the Point-of-Care

Lateral Flow Immunoassays (LFIAs) are biosensors, which among others are used for the detection of infectious diseases. Due to their numerous advantages, they are particularly suitable for point of care testing, especially in developing countries where there is lack of medical healthcare centers and trained personnel. When the testing sample is positive, the LFIAs generate a color test line to indicate the presence of analyte. The intensity of the test line relates to the concentration of analyte. Even though the color test line can be visually observed for the accurate quantification of the results in LFIAs an external electronic reader is required. Existing readers are not fully optimized for point-of-care (POC) testing and therefore have significant limitations. This thesis presents the development of three readout systems that quantify the results of LFIAs. The first system was implemented as a proof of concept of the proposed method, which is based on the scanning approach without using any moving components or any extra optical accessories. Instead, the test line and the area around it, are scanned using an array of photodiodes (1 × 128). The small size of the pixels gives the system sufficient spatial resolution, to avoid errors due to positioning displacement of the strip. The system was tested with influenza A nucleoprotein and the results demonstrate its quantification capabilities. The second generation system is an optimized version of the proof of concept system. Optimization was performed in terms of matching the photodetectors wavelength with the maximum absorption wavelength of the gold nanoparticles presented in the tested LFIA. Ray trace simulations defined the optimum position of all the components in order to achieve uniform light distribution across the LFIA with the minimum number of light sources. An experimental model of the optical profile of the surface of LFIA was also generated for accurate simulations. Tests of the developed system with LFIAs showed its ability to quantify the results while having reduced power consumption and better limit of detection compared to the first system. Finally, a third generation system was realized which demonstrated the capability of having a miniaturized reader. The photodetector of the previous systems was replaced with a CMOS Image Sensor (CIS), specifically designed for this application. The pixel design was optimized for very low power consumption via biasing the transistors in subthreshold and by reusing the same amplifier for both photocurrent to voltage conversion and noise cancellation. With uniform light distribution at 525 nm and 76 frames/s the chip has 1.9 mVrms total output referred noise and a total power consumption of 21 μW. In tests with lateral flow immunoassay, this system detected concentrations of influenza A nucleoprotein from 0.5 ng/mL to 200 ng/mL

On the Privacy and Utility of Social Networks

Ph.DDOCTOR OF PHILOSOPH