Large scale social data from online social networks, instant messaging applications, and wearable devices have seen an exponential growth in a number of users and activities recently. The rapid proliferation of social data provides rich information and infinite possibilities for us to understand and analyze the complex inherent mechanism which governs the evolution of the new technology age. Influence, as a natural product of information diffusion (or propagation), which represents the change in an individual’s thoughts, attitudes, and behaviors resulting from interaction with others, is one of the fundamental processes in social worlds. Therefore, influence analysis occupies a very prominent place in social related data analysis, theory, model, and algorithms. In this dissertation, we study the influence analysis under the scenario of big social data. Firstly, we investigate the uncertainty of influence relationship among the social network. A novel sampling scheme is proposed which enables the development of an efficient algorithm to measure uncertainty. Considering the practicality of neighborhood relationship in real social data, a framework is introduced to transform the uncertain networks into deterministic weight networks where the weight on edges can be measured as Jaccard-like index. Secondly, focusing on the dynamic of social data, a practical framework is proposed by only probing partial communities to explore the real changes of a social network data. Our probing framework minimizes the possible difference between the observed topology and the actual network through several representative communities. We also propose an algorithm that takes full advantage of our divide-and-conquer strategy which reduces the computational overhead. Thirdly, if let the number of users who are influenced be the depth of propagation and the area covered by influenced users be the breadth, most of the research results are only focused on the influence depth instead of the influence breadth. Timeliness, acceptance ratio, and breadth are three important factors that significantly affect the result of influence maximization in reality, but they are neglected by researchers in most of time. To fill the gap, a novel algorithm that incorporates time delay for timeliness, opportunistic selection for acceptance ratio, and broad diffusion for influence breadth has been investigated. In our model, the breadth of influence is measured by the number of covered communities, and the tradeoff between depth and breadth of influence could be balanced by a specific parameter. Furthermore, the problem of privacy preserved influence maximization in both physical location network and online social network was addressed. We merge both the sensed location information collected from cyber-physical world and relationship information gathered from online social network into a unified framework with a comprehensive model. Then we propose the resolution for influence maximization problem with an efficient algorithm. At the same time, a privacy-preserving mechanism are proposed to protect the cyber physical location and link information from the application aspect. Last but not least, to address the challenge of large-scale data, we take the lead in designing an efficient influence maximization framework based on two new models which incorporate the dynamism of networks with consideration of time constraint during the influence spreading process in practice. All proposed problems and models of influence analysis have been empirically studied and verified by different, large-scale, real-world social data in this dissertation.

Submitted by Neusa Fagundes (neusa.fagundes@unioeste.br) on 2018-06-18T14:36:28Z No. of bitstreams: 2 Daniela_Nava2018.pdf: 3424820 bytes, checksum: 89e78787f114c44f669182c6285080ae (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-06-18T14:36:28Z (GMT). No. of bitstreams: 2 Daniela_Nava2018.pdf: 3424820 bytes, checksum: 89e78787f114c44f669182c6285080ae (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-02-02 / This tesis aimed at studying spatial discrete distributions based on two different points of view, that are, spatial point processes and spatial correlated binomial distribution. The data set came from an experiment setted in an agricultural commercial area in Cascavel city Paraná State, cropped with corn. The experimental area was subdivided into 40 georeferenced patch of land and the number of plants infected by Spodoptera frugiperda was observed within each patch of land. Thus, it is assumed that the data set have a binomial distribution. A study of first order local influence was proposed in order to verify possible influential points. The results suggest that the presence of influential observations in the data set have changed the statistical inference, the predicted values and the respective maps. In a second study, our interest was the spatial distribution of the fall armyworm in the experimental area. In order to do that, we used spatial point processes, where each plant infected by the insect within the experimental area was considered as an event of interest. An anisotropy study was carried out using different point process techniques, such as K directional function and wavelet test. The results show that the spatial distribution of the fall armyworm follow a Poisson cluster process with an evident anisotropy, mainly due to the shape of the experimental area. / O objetivo deste trabalho foi discutir distribuições discretas espaciais utilizando pontos de vista distintos, a saber, processos pontuais espaciais e distribuição binomial para dados espacialmente correlacionados. Os dados utilizados são provenientes de um experimento agrícola implantado em uma área comercial agrícola no município de Cascavel, estado do Paraná, cultivada com a cultura do milho. Subdividiu-se a área experimental em 40 parcelas georeferenciadas e observou-se o número de plantas atacadas pela lagarta do cartucho, do total de plantas de cada parcela. Para tal, assumiu-se que os dados possuem distribuição binomial. Propôs-se um estudo de análise de influência local de primeira ordem com o interesse em verificar possíveis pontos influentes. Os resultados obtidos sugerem que a presença de observações influentes nos dados modificam a inferência estatística, os valores preditos e os respectivos mapas. Em um segundo estudo, que teve como interesse a distribuição espacial da lagarta do cartucho na área experimental, utilizou-se de ferramentais de estatística espacial pontual. Para tal, cada planta infectada pelo inseto dentro da área experimental foi considerada como um evento de interesse. Realizou-se um estudo de anisotropia a partir de diferentes técnicas de processos pontuais, como K direcional e teste de ondaletas. Os resultados mostraram que a distribuição espacial da lagarta segue um processo pontual de Poisson agrupado com evidente anisotropia principalmente devido à forma da área experimental.

The aim of this work is to present an inference and diagnostic study of an extension of the lifetime distribution family proposed by Birnbaum and Saunders (1969a,b). This extension is obtained by considering a skew-elliptical distribution instead of the normal distribution. Specifically, in this work we develop a Birnbaum-Saunders (BS) distribution type based on scale mixtures of skew-normal distributions (SMSN). The resulting family of lifetime distributions represents a robust extension of the usual BS distribution. Based on this family, we reproduce the usual properties of the BS distribution, and present an estimation method based on the EM algorithm. In addition, we present regression models associated with the BS distributions (based on scale mixtures of skew-normal), which are developed as an extension of the sinh-normal distribution (Rieck and Nedelman, 1991). For this model we consider an estimation and diagnostic study for uncensored data. / O objetivo deste trabalho é apresentar um estudo de inferência e diagnóstico em uma extensão da família de distribuições de tempos de vida proposta por Birnbaum e Saunders (1969a,b). Esta extensão é obtida ao considerar uma distribuição skew-elíptica em lugar da distribuição normal. Especificamente, neste trabalho desenvolveremos um tipo de distribuição Birnbaum-Saunders (BS) baseda nas distribuições mistura de escala skew-normal (MESN). Esta família resultante de distribuições de tempos de vida representa uma extensão robusta da distribuição BS usual. Baseado nesta família, vamos reproduzir as propriedades usuais da distribuição BS, e apresentar um método de estimação baseado no algoritmo EM. Além disso, vamos apresentar modelos de regressão associado à distribuições BS (baseada na distribuição mistura de escala skew-normal), que é desenvolvida como uma extensão da distribuição senh-normal (Rieck e Nedelman, 1991), para estes vamos considerar um estudo de estimação e diagnóstisco para dados sem censura.

The aim of this work is to present an inference and diagnostic study of an extension of the lifetime distribution family proposed by Birnbaum and Saunders (1969a,b). This extension is obtained by considering a skew-elliptical distribution instead of the normal distribution. Specifically, in this work we develop a Birnbaum-Saunders (BS) distribution type based on scale mixtures of skew-normal distributions (SMSN). The resulting family of lifetime distributions represents a robust extension of the usual BS distribution. Based on this family, we reproduce the usual properties of the BS distribution, and present an estimation method based on the EM algorithm. In addition, we present regression models associated with the BS distributions (based on scale mixtures of skew-normal), which are developed as an extension of the sinh-normal distribution (Rieck and Nedelman, 1991). For this model we consider an estimation and diagnostic study for uncensored data. / O objetivo deste trabalho é apresentar um estudo de inferência e diagnóstico em uma extensão da família de distribuições de tempos de vida proposta por Birnbaum e Saunders (1969a,b). Esta extensão é obtida ao considerar uma distribuição skew-elíptica em lugar da distribuição normal. Especificamente, neste trabalho desenvolveremos um tipo de distribuição Birnbaum-Saunders (BS) baseda nas distribuições mistura de escala skew-normal (MESN). Esta família resultante de distribuições de tempos de vida representa uma extensão robusta da distribuição BS usual. Baseado nesta família, vamos reproduzir as propriedades usuais da distribuição BS, e apresentar um método de estimação baseado no algoritmo EM. Além disso, vamos apresentar modelos de regressão associado à distribuições BS (baseada na distribuição mistura de escala skew-normal), que é desenvolvida como uma extensão da distribuição senh-normal (Rieck e Nedelman, 1991), para estes vamos considerar um estudo de estimação e diagnóstisco para dados sem censura.