PRETÉRITO IMPERFEITO DE TERRITÓRIOS MÓVEIS é uma pesquisa que busca diferentes maneiras de explorar fotograficamente o rosto - e até a ausência dele -, no universo dos álbuns de Redes Sociais. As experimentações poéticas se dividem nas séries EGOSHOTS, BIOSHOTS e THERE'S NO PLACE LIKE 127.0.0.1., criando-se imagens que são chaves, portas e espelhos que refletem o eu, o outro e o lugar. Tendo como mote os conceitos de Rostidade e de Nomadismo de Gilles Deleuze & Felix Guattari e de Michel Maffesoli, cada série propõe pensar o rosto como um território que migra conforme os fluxos de interação social, e como tal adotá-lo como uma espécie de plataforma para múltiplas inscrições. Os rostos que se apresentam nesses ambientes virtuais são móveis e multifacetados. São muitos como se fossem um só, e únicos em suas particularidades. São fragmentos visuais de territórios móveis, de passado incerto, presente inconcluso e futuro fragmentado em pixels. / THE PAST IMPERFECT OF MOBILE TERRITORIES is a research which aims to photographically explore different ways to the face - and even the lack of face - of the universe of a/bums of Social Networks. The trials are divided into the poetic series EGOSHOTS, BIOSHOTS and THERE'S NO PLACE LIKE 127.0.0.1., creating images that are keys, doors and mirrors that reflect the se/f and the other place. Having as its the concepts of Faciality and Nomadism of Gi//es Deleuze & Felix Guattari and Michel Maffesoli, each series proposes to discuss the face as a territory which migrates as the flow of social interaction, and therefore adopt it as a kind of platform for multiple applications. The faces that appear in these virtual environments are mobile and multifaceted. Many as if they were one and unique in their particulars. They are fragments of territory visual furniture, obscure past, present and future unfinished fragmented into pixels.

abstract: The rapid urban expansion has greatly extended the physical boundary of our living area, along with a large number of POIs (points of interest) being developed. A POI is a specific location (e.g., hotel, restaurant, theater, mall) that a user may find useful or interesting. When exploring the city and neighborhood, the increasing number of POIs could enrich people's daily life, providing them with more choices of life experience than before, while at the same time also brings the problem of "curse of choices", resulting in the difficulty for a user to make a satisfied decision on "where to go" in an efficient way. Personalized POI recommendation is a task proposed on purpose of helping users filter out uninteresting POIs and reduce time in decision making, which could also benefit virtual marketing. Developing POI recommender systems requires observation of human mobility w.r.t. real-world POIs, which is infeasible with traditional mobile data. However, the recent development of location-based social networks (LBSNs) provides such observation. Typical location-based social networking sites allow users to "check in" at POIs with smartphones, leave tips and share that experience with their online friends. The increasing number of LBSN users has generated large amounts of LBSN data, providing an unprecedented opportunity to study human mobility for personalized POI recommendation in spatial, temporal, social, and content aspects. Different from recommender systems in other categories, e.g., movie recommendation in NetFlix, friend recommendation in dating websites, item recommendation in online shopping sites, personalized POI recommendation on LBSNs has its unique challenges due to the stochastic property of human mobility and the mobile behavior indications provided by LBSN information layout. The strong correlations between geographical POI information and other LBSN information result in three major human mobile properties, i.e., geo-social correlations, geo-temporal patterns, and geo-content indications, which are neither observed in other recommender systems, nor exploited in current POI recommendation. In this dissertation, we investigate these properties on LBSNs, and propose personalized POI recommendation models accordingly. The performance evaluated on real-world LBSN datasets validates the power of these properties in capturing user mobility, and demonstrates the ability of our models for personalized POI recommendation. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2014

abstract: A myriad of social media services are emerging in recent years that allow people to communicate and express themselves conveniently and easily. The pervasive use of social media generates massive data at an unprecedented rate. It becomes increasingly difficult for online users to find relevant information or, in other words, exacerbates the information overload problem. Meanwhile, users in social media can be both passive content consumers and active content producers, causing the quality of user-generated content can vary dramatically from excellence to abuse or spam, which results in a problem of information credibility. Trust, providing evidence about with whom users can trust to share information and from whom users can accept information without additional verification, plays a crucial role in helping online users collect relevant and reliable information. It has been proven to be an effective way to mitigate information overload and credibility problems and has attracted increasing attention. As the conceptual counterpart of trust, distrust could be as important as trust and its value has been widely recognized by social sciences in the physical world. However, little attention is paid on distrust in social media. Social media differs from the physical world - (1) its data is passively observed, large-scale, incomplete, noisy and embedded with rich heterogeneous sources; and (2) distrust is generally unavailable in social media. These unique properties of social media present novel challenges for computing distrust in social media: (1) passively observed social media data does not provide necessary information social scientists use to understand distrust, how can I understand distrust in social media? (2) distrust is usually invisible in social media, how can I make invisible distrust visible by leveraging unique properties of social media data? and (3) little is known about distrust and its role in social media applications, how can distrust help make difference in social media applications? The chief objective of this dissertation is to figure out solutions to these challenges via innovative research and novel methods. In particular, computational tasks are designed to {\it understand distrust}, a innovative task, i.e., {\it predicting distrust} is proposed with novel frameworks to make invisible distrust visible, and principled approaches are develop to {\it apply distrust} in social media applications. Since distrust is a special type of negative links, I demonstrate the generalization of properties and algorithms of distrust to negative links, i.e., {\it generalizing findings of distrust}, which greatly expands the boundaries of research of distrust and largely broadens its applications in social media. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2015

abstract: This descriptive research used social network analysis to explore the influence of relationships and communication among hospital nursing (RN, LPN, CNA) and discharge planning staff on adherence to evidence-based practices (EBP) for reducing preventable hospital readmissions. Although previous studies have shown that nurses are a valued source of research information for each other, there have been few studies concerning the role that staff relationships and communication play in adherence to evidence-based practice. The investigator developed the Relational Model of Communication and Adherence to EBP from diffusion of innovation theory, social network theories, relational coordination theory, and quality improvement literature. The study sample consisted of 10 adult-medical surgical units, five home care agencies and six long-term care facilities. A total of 273 hospital nursing and discharge planning staff and 69 post-acute staff participated. Hospital staff completed a survey about communication patterns for patient care and patient discharge and about communication quality on the unit. Hospital and post-acute care staff completed surveys about relationship quality and demographic characteristics. Evidence-based practice adherence rates for risk assessment, medication reconciliation, and discharge summary were measured as documented in the electronic medical record. Social network analysis was used to analyze the communication patterns for patient care communication at the unit. These findings were correlated with (1) aggregate responses for communication quality, (2) aggregate responses for relationship quality, and (3) EBP adherence. Statistically significant relationships were found between communication patterns, and communication quality and relationship quality. There were ii two significant relationships between communication quality, and EBP adherence. Limitations in response rates and missing data prevented the analysis of all of the hypothesized relationships. The findings from this study provide empirical support for the role of social networks and relationships among staff in adoption of, and adherence to, EBP. Social network theory and social network analysis, especially the concept of knowledge sharing, provide ways to understand and leverage the influence of peer relationships. Future studies are needed to better understand the contribution that relationships among staff (social networks) have in the adoption of and adherence to EBP among nursing staff. Further model development and multilevel studies are / Dissertation/Thesis / Doctoral Dissertation Nursing and Healthcare Innovation 2016

abstract: Node proximity measures are commonly used for quantifying how nearby or otherwise related to two or more nodes in a graph are. Node significance measures are mainly used to find how much nodes are important in a graph. The measures of node proximity/significance have been highly effective in many predictions and applications. Despite their effectiveness, however, there are various shortcomings. One such shortcoming is a scalability problem due to their high computation costs on large size graphs and another problem on the measures is low accuracy when the significance of node and its degree in the graph are not related. The other problem is that their effectiveness is less when information for a graph is uncertain. For an uncertain graph, they require exponential computation costs to calculate ranking scores with considering all possible worlds. In this thesis, I first introduce Locality-sensitive, Re-use promoting, approximate Personalized PageRank (LR-PPR) which is an approximate personalized PageRank calculating node rankings for the locality information for seeds without calculating the entire graph and reusing the precomputed locality information for different locality combinations. For the identification of locality information, I present Impact Neighborhood Indexing (INI) to find impact neighborhoods with nodes' fingerprints propagation on the network. For the accuracy challenge, I introduce Degree Decoupled PageRank (D2PR) technique to improve the effectiveness of PageRank based knowledge discovery, especially considering the significance of neighbors and degree of a given node. To tackle the uncertain challenge, I introduce Uncertain Personalized PageRank (UPPR) to approximately compute personalized PageRank values on uncertainties of edge existence and Interval Personalized PageRank with Integration (IPPR-I) and Interval Personalized PageRank with Mean (IPPR-M) to compute ranking scores for the case when uncertainty exists on edge weights as interval values. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2017

abstract: Diffusion processes in networks can be used to model many real-world processes, such as the propagation of a rumor on social networks and cascading failures on power networks. Analysis of diffusion processes in networks can help us answer important questions such as the role and the importance of each node in the network for spreading the diffusion and how to top or contain a cascading failure in the network. This dissertation consists of three parts. In the first part, we study the problem of locating multiple diffusion sources in networks under the Susceptible-Infected-Recovered (SIR) model. Given a complete snapshot of the network, we developed a sample-path-based algorithm, named clustering and localization, and proved that for regular trees, the estimators produced by the proposed algorithm are within a constant distance from the real sources with a high probability. Then, we considered the case in which only a partial snapshot is observed and proposed a new algorithm, named Optimal-Jordan-Cover (OJC). The algorithm first extracts a subgraph using a candidate selection algorithm that selects source candidates based on the number of observed infected nodes in their neighborhoods. Then, in the extracted subgraph, OJC finds a set of nodes that "cover" all observed infected nodes with the minimum radius. The set of nodes is called the Jordan cover, and is regarded as the set of diffusion sources. We proved that OJC can locate all sources with probability one asymptotically with partial observations in the Erdos-Renyi (ER) random graph. Multiple experiments on different networks were done, which show our algorithms outperform others. In the second part, we tackle the problem of reconstructing the diffusion history from partial observations. We formulated the diffusion history reconstruction problem as a maximum a posteriori (MAP) problem and proved the problem is NP hard. Then we proposed a step-by- step reconstruction algorithm, which can always produce a diffusion history that is consistent with the partial observations. Our experimental results based on synthetic and real networks show that the algorithm significantly outperforms some existing methods. In the third part, we consider the problem of improving the robustness of an interdependent network by rewiring a small number of links during a cascading attack. We formulated the problem as a Markov decision process (MDP) problem. While the problem is NP-hard, we developed an effective and efficient algorithm, RealWire, to robustify the network and to mitigate the damage during the attack. Extensive experimental results show that our algorithm outperforms other algorithms on most of the robustness metrics. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2018

abstract: Grounded in the relational view of leadership, this dissertation explores the dynamics of the leader/follower relationship in the context of a collective using a social networks approach. Specifically, I build on DeRue and Ashford’s (2010) work that focuses on dynamic, socially constructed leadership relationships within a dyad to focus on such relationships within a collective. In doing so, I conceptualize collective leader endorsement – receiving a grant of leader identity from a collective of followers – and examine the implications of collective leader endorsement. As a dynamic relationship, collective leader endorsement can change as individuals give and receive grants of leader identity. I draw on relational models of leadership theory and appraisal theory to examine how contextual situations (i.e., identity jolts) prompt change in collective leader endorsement at the network level and how such change can influence individual functioning at the individual level. As a socially constructed relationship, collective leader endorsement creates the potential for disagreement among members of the collective regarding grants of leader identity. I draw on social comparison theory and appraisal theory to suggest that agreement (or lack thereof) can influence the individual’s perceived demands and overall functioning within the collective. Using data from 106 individuals on a collegiate football team in the United States over 12 consecutive weeks, I find significant changes in collective leader endorsement and the associated leadership network over the course of the season. Specifically, I find that challenging situations prompted a reevaluation of leader identities and shifted the patterns within the leadership network. In addition, change in an individual’s level of collective leader endorsement prompted additional perceived demands and lowered well-being. This relationship was attenuated if the individual had a supportive coach to help him cope with additional leadership demands. Finally, (lack of) agreement regarding the individual’s leader identity also influenced the individual’s well-being. Specifically, the individual experienced enhanced perceived demands (and associated lower well-being) if the individual’s perception of who should receive grants of leader identity was incongruent with the collective’s perception of collective leader endorsement. / Dissertation/Thesis / Doctoral Dissertation Business Administration 2018

Submitted by Valquíria Barbieri (kikibarbi@hotmail.com) on 2017-06-01T21:05:28Z No. of bitstreams: 1 DISS_2014_Joana Rodrigues Moreira-Leite.pdf: 2887251 bytes, checksum: 1719ed3bfa5bda77b694b7dfbcd05725 (MD5) / Approved for entry into archive by Jordan (jordanbiblio@gmail.com) on 2017-06-07T13:42:04Z (GMT) No. of bitstreams: 1 DISS_2014_Joana Rodrigues Moreira-Leite.pdf: 2887251 bytes, checksum: 1719ed3bfa5bda77b694b7dfbcd05725 (MD5) / Made available in DSpace on 2017-06-07T13:42:04Z (GMT). No. of bitstreams: 1 DISS_2014_Joana Rodrigues Moreira-Leite.pdf: 2887251 bytes, checksum: 1719ed3bfa5bda77b694b7dfbcd05725 (MD5) Previous issue date: 2014-09-02 / O objetivo desta dissertação é investigar por meio da comunidade “Diário de Classe”, existente no site Facebook, como uma aluna da contemporaneidade e usuária de redes sociais constrói, em seus discursos, a escola atual. O trabalho encontrou suporte teórico em Sibilia (2012), Tapscott (2010), Jesus (2007), Castells (1999), Castells & Cardoso (2005), Fairclough (2001), Scherrer-Warren (2011), Recuero (2009), Santaella & Lemos (2010), Santaella (2010), Rosa & Santos (2013), Hall (1997), Hall & Woodward (2000), Foucault (1987; 1998; 2012), Bourdieu (1989), Bourdieu & Passeron (2009), Rojo (2009, 2012), Lanshear & Knobel (2006), Cope & Kalantzis (2000), Monte Mór (2013), Coffey (2010) e Maciel (2013), entre outros. Estas as perguntas que nortearam a pesquisa: 1) De que forma a aluna da comunidade “Diário de Classe” constrói seus discursos à volta da escola contemporânea? 2) Como a escola reage aos discursos proferidos pela discente? 3) Qual é o sentido conferido às tecnologias digitais por esta discente? A metodologia de pesquisa é de base interpretativista nas perspectivas de Flick (2009) e Bortoni-Ricardo (2008). Os resultados sugerem que o discurso da discente mostrou-se contraditório em alguns pontos porque, na maioria dos dados, houve indícios de desejar uma escola com ideais de verdade e punição. O professor é o ator principal do processo de ensino-aprendizagem em privilégio aos letramentos formais, e as mudanças solicitadas reproduziam os discursos hegemônicos. Apesar de a aluna ter alguns discursos semelhantes aos da escola, esta entendeu que a ação da discente foi uma afronta as normas escolares e reagiu contra o discurso da estudante. Ao final surgem sinais, nos discursos da aluna de que as tecnologias digitais exercem relevância em sua vida porque, por meio delas, ela pode aprender coisas que a escola não a ensina. Portanto, concluímos que ainda há muito que discutir sobre os discursos escolares para construção de desenhos mais significativos para a educação do presente, dando espaço a poderes mais distribuídos entre os sujeitos escolares, rumo a que se valorizem os letramentos múltiplos, multiletramentos e letramentos críticos. / The aim of this thesis is to investigate through the community "Diary of Class" on the Facebook website, how a contemporary student and user of social networks has built in her discourse about current school. The main theoretical underpinning for the research is provided by Sibilia (2012), Tapscott (2010), Jesus (2007), Castells (1999), Castells & Cardoso (2005), Fairclough (2001), Scherrer-Warren (2011), Recuero (2009), Santaella & Lemos (2010), Santaella (2010), Rosa & Santos (2013), Hall (1997), Hall & Woodward (2000), Foucault (1987, 1998, 2012), Bourdieu (1989), Bourdieu & Passeron, (2009), Rojo (2009, 2012), Lanshear & Knobel (2006), Cope & Kalantzis (2000), Monte Mór (2013), Coffey (2010) and Maciel (2013) among others. The research questions are the following: 1) How does the student of the community "Diary of Class" build her discourse concerning about contemporary school? 2) How does the school react about the discourse of the student? 3) What is the meaning given to digital technologies for this student? The research methodology is based on interpretative approach (Flick, 2009; Bortoni-Ricardo, 2008). The results suggest that the student has had a contradictory discourse in some points, there were evidences of keeping a school with truth and discipline ideals. The teacher is the main actor in the teaching/learning process in a formal literacies perspective, and the changes requested reproduced hegemonic discourse. Even though the student has had some similar discourses to school, it has understood that the action of the student has been an affront to the school rules and it reacted against the discourse of the student. At the end, signals come up, in the student discourse, that digital technologies have an important bearing in her life because, by means of them, she could learn things that the school does not teach for her. Therefore, we conclude that there is still a lot to be discussed about school discourse for the construction of more meaningful drawings concerning the current education, giving place to more distributed power between school members, towards the increase in value of the multiple literacies, multiliteracies and critical literacies.

Prédire la diffusion de l'information dans les réseaux sociaux est une tâche difficile qui peut cependant permettre de répondre à des problèmes intéressants : recommandation d'information, choix des meilleurs points d'entrée pour une diffusion, etc. La plupart des modèles proposés récemment sont des extensions des modèles à cascades et de seuil. Dans ces modèles, le processus de diffusion est basé sur les interactions entre les utilisateurs du réseau (la pression sociale), et ignore des caractéristiques importantes comme le contenu de l'information diffusé ou le rôle actif/passif des utilisateurs. Nous proposons une nouvelle famille de modèles pour prédire la façon dont le contenu se diffuse dans un réseau en prenant en compte ces nouvelles caractéristiques : le contenu diffusé, le profil des utilisateurs et leur tendance à diffuser. Nous montrons comment combiner ces caractéristiques et proposons une modélisation probabiliste pour résoudre le problème de la diffusion. Ces modèles sont illustrés et comparés avec d'autres approches sur deux jeux de données de blogs. Les résultats obtenus sur ces jeux de données montrent que prendre en compte ces caractéristiques est important pour modéliser le processus de diffusion. Enfin, nous étudions le problème de maximisation de l'influence avec ces modèles et prouvons qu'il est NP-difficile, avant de proposer une adaptation d'un algorithme glouton pour approcher la solution optimale. / Predicting the diffusion of information in social networks is a key problem for applications like Opinion Leader Detection, Buzz Detection or Viral Marketing. Many recent diffusion models are direct extensions of the Cascade and Threshold models, initially proposed for epidemiology and social studies. In such models, the diffusion process is based on the dynamics of interactions between neighbor nodes in the network (the social pressure), and largely ignores important dimensions as the content diffused and the active/passive role users tend to have in social networks. We propose here a new family of models that aims at predicting how a content diffuses in a network by making use of additional dimensions : the content diffused, user's profile and willingness to diffuse. In particular, we show how to integrate these dimensions into simple feature functions, and propose a probabilistic modeling to account for the diffusion process. These models are then illustrated and compared with other approaches on two blog datasets. The experimental results obtained on these datasets show that taking into account these dimensions are important to accurately model the diffusion process. Lastly, we study the influence maximization problem with these models and prove that it is NP-hard, prior to propose an adaptation of the greedy algorithm to approximate the optimal solution.

On the Swedish labor market, occupational segregation has decreased during subsequent decades of the 20th century. However, it remains one of the most gender segregated labor markets in Europe. The reproduction of occupational segregation is considered a result of the intersection between structural and individual factors. In studying youth’s social capital extensity and occupational choice as well as the pathway in between, the present study fills a research gap in demonstrating a gendered nature of social capital in a country praised for its gender equity. Using LPM regression analyses on panel data acquired from the 2009 and 2013 waves of the Swedish survey Social capital and labor market integration: A cohort study, the results show support for the existence of gender differences both in accessed social capital and its influence on occupational choice. Seemingly, close members of social networks facilitate men and women into different occupations. The importance of social capital extensity however, is only present when choosing a male-dominated occupation, which is interpreted as that the preferred informal job search method is more often used in the private sector were most male-dominated occupations are found.