This thesis reports on the social structures underpinning interschool collaboration in the context of Shared Education and the networks of staff relations that have been developed for the purpose of overcoming systemic separation. Drawing upon social network theory, it is argued that in order to further the model of Shared Education the corresponding research and academic enterprise must move beyond the analogous use of the term ‘network’ and consider the concept in a more analytical manner. In this sequential mixed methods case study, an exploratory network analysis of the staff members (n=97) from five collaborating primary schools in a Shared Education partnership was performed using a socio-metric instrument to examine four collaborative interactions—exchanging resources, seeking professional knowledge, discussing personal matters, and meeting socially. This was then followed by semi-structured interviews with the staff members (n=16) observed as most central within the partnership's network. The findings of this study suggest that Shared Education can facilitate network structures that overcome systemic separation; that partner preference is based upon desirable structural characteristics; that partnership sustainability may be an extension of social network adaptability; that Shared Education offered an alternative model for collegial engagement; that the model can facilitate learning relationships and knowledge creation; and that relational embeddedness is also observed to be a critical aspect of the partnership's leadership. Therefore, it is advocated that those researching Shared Education must develop a more nuanced approach to thinking about the structure of partnerships and the relationships that constitute them.

abstract: Most new first-year composition (FYC) students already have a great deal of writing experience. Much of this experience comes from writing in digital spaces, such as Facebook, Twitter, Instagram, and Pinterest. This type of writing is often invisible to students: they may not consider it to be writing at all. This dissertation seeks to better understand the actual connections between writing in online spaces and writing in FYC, to see the connections students see between these types of writing, and to work toward a theory for making use of those connections in the FYC classroom. The following interconnected articles focus specifically on Facebook--the largest and most ubiquitous social network site (SNS)-- as a means to better understand students' digital literacy practices. Initial data was gathered through a large-scale survey of FYC students about their Facebook use and how they saw that use as connected to composition and writing. Chapter 1 uses the data to suggest that FYC students are not likely to see a connection between Facebook and FYC but that such a connection exists. The second chapter uses the same data to demonstrate that men and women are approaching Facebook slightly differently and to explore what that may mean for FYC teachers. The third chapter uses 10 one-on-one interviews with FYC students to further explore Facebook literacies. The interviews suggest that the literacy of Facebook is actually quite complex and includes many modes of communication in addition to writing, such as pictures, links, and "likes." The final chapter explores the issue of transfer. While transfer is popular in composition literature, studies tend to focus on forward-reading and not backward-reaching transfer. This final chapter stresses the importance of this type of transfer, especially when looking back at digital literacy knowledge that students have gained through writing online. While these articles are intended as stand-alone pieces, together they demonstrate the complex nature of literacies on Facebook, how they connection to FYC, and how FYC teachers may use them in their classrooms. They serve as a starting off point for discussions of effective integration of digital literacies into composition pedagogies. / Dissertation/Thesis / Doctoral Dissertation English 2014

abstract: This dissertation explored how immigrants cope with and thrive in old age by utilizing social networks, and the hindrances which may prevent this. Through ethnographic fieldwork and in-depth interviews at two senior centers in Phoenix, Arizona with a high concentration of an ethnic minority group - Asian and Latino, I describe what makes the Asian dominant center more resource abundant than its Latino counterpart given prevalent tight public funding. Both centers have a large number of seniors disenfranchised from mainstream institutions who bond together via similar experiences resulting from shared countries/regions of origin, language, and migration experience. The Asian center, however, is more successful in generating and circulating resources through "bonding" and "bridging" older immigrants who, therefore benefit more from their center affiliation than the Latinos at their center. The abundance of resources at the Asian center flowing to the social networks of seniors are attributed to three factors: work and volunteer engagement and history, the organization of the center, and individual activities. At both centers seniors bond with each other due to shared ethnicity, language, and migration experience and share information and companionship in the language in which they feel most comfortable. What differentiated the two centers were the presence of several people well connected to individuals, groups, and institutions beyond the affiliated center. The presence of these "bridges" were critical when the centers were faced with budgetary constraints and Arizona was experiencing the effect of ongoing immigration policies. These "bridges" tend to come from shared ethnicity, and better social positions due to cumulative factors which include but are not limited to higher education, professional occupation, and work and volunteer history. I have also presented cases of individuals who, although have developed expertise from past work experiences and individual activities, have limited contribution to the resource flow because of the differences in ethnicity. The study also explored a gendered life course and its impact on the social network for older Asian and Latino immigrants. / Dissertation/Thesis / Doctoral Dissertation Sociology 2014

abstract: With the rise of social media, hundreds of millions of people spend countless hours all over the globe on social media to connect, interact, share, and create user-generated data. This rich environment provides tremendous opportunities for many different players to easily and effectively reach out to people, interact with them, influence them, or get their opinions. There are two pieces of information that attract most attention on social media sites, including user preferences and interactions. Businesses and organizations use this information to better understand and therefore provide customized services to social media users. This data can be used for different purposes such as, targeted advertisement, product recommendation, or even opinion mining. Social media sites use this information to better serve their users. Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2014

abstract: People are motivated to participate in musical activities for many reasons. Whereas musicians may be driven by an intrinsic desire for musical growth, self-determination theory suggests that this drive must also be sustained and supported by the social environment. Social network analysis is an interdisciplinary theoretical framework and collection of analytical methods that allows us to describe the social context of a musical ensemble. These frameworks are utilized to investigate the relationship of participatory motivation and social networks in a large Division I collegiate marching band. This study concludes that marching band members are predominantly self-determined to participate in marching band and are particularly motivated for social reasons, regardless of their experience over the course of the band season. The members who are highly motived are also more integrated into the band's friendship and advice networks. These highly integrated members also tend to be motivated by the value and importance others display for the marching band activity suggesting these members have begun to internalized those values and seek out others with similar viewpoints. These findings highlight the central nature of the social experience of marching band and have possible implications for other musical leisure ensembles. After a brief review of social music making and the theoretical frameworks, I will provide illustrations of the relationship between motivation and social networks in a musical ensemble, consider the implications of these findings for promoting self-determined motivation and the wellbeing of musical ensembles, and identify directions for future research. / Dissertation/Thesis / Doctoral Dissertation Music 2015

abstract: Background: College freshmen are exposed to a variety of environmental and social factors that can alter changes to health habits and encourage weight gain. Weight-related conversations had with friends may be related to self-perception of weight and alterations to health behaviors, but this association has yet to be assessed in the college population. Objective: This study aims to examine the relationship between friend advice about weight management, self-perception of weight, and alterations to weight change intentions, physical activity, and eating habits in college freshmen over time. Methods: College freshmen from ASU with complete data for three time points (n=321) were found to be predominantly female (72.2%) and non-white (53.2%) with a mean age of 17.5±41. Complete data included responses for items included in analysis which were related to friend encouragement about weigh management, self-perception of weight, physical activity, eating behaviors, and weight change intentions. A longitudinal multivariate mediation analysis using negative binomial regression adjusted for sociodemographics and clustering by dorm was used to assess the relationship between 1) friend encouragement about weight management at time 1 and behavioral outcomes at time 3, 2) friend encouragement about weight management at time 1 and self-perception of weight at time 2, and 3) self-perception of weight at time 2 and behavioral outcomes at time 3. Results: A small proportion of population perceived friend encouragement about weight loss (18.3%) and weight gain (14.4%) at time 1. Half the population (50.9%) had the self-perception of overweight at time 2. At time 3, more than half (54.3%) of individuals performed at least 60 minutes of MVPA and consumed at least ½ a serving of sugar-sweetened beverages each day, while nearly half (48.6%) consumed at least 2 servings of fruits and vegetables each day. Males perceived more friend encouragement to gain weight (27.4%; p<0.01), but more females had the self-perception of overweight (54%; p=0.04) and were attempting to lose weight (59.3%; p<0.01). Individuals who perceived friend encouragement to lose weight at time 1 had a 14.8% greater prevalence (p<0.001) of overweight perception of time two, and a 9.6% and 6.9%; decreased prevalence (p<0.001) of weight change and weight loss intentions (p=0.023) at time three respectively. Individuals who perceived friend encouragement to gain weight had a 34.9% decreased prevalence of (p<0.001) of self-perception of overweight at time 1. In individuals with the self-perception of overweight at time 2, there was a 18.1% increased prevalence (p<0.001) of consuming at least ½ a serving of sugar-sweetened beverages/day and an increased prevalence of 22.8% and 24.0% for weight change intentions and weight loss intentions at time 3 (p<0.001). Conclusion: These findings suggest that there was not a mediation effect of self-perception of overweight in the relationship between friend encouragement about weight management and behavioral outcomes in the current sample. However, the increased prevalence of overweight perception in individuals who perceived friend encouragement about weight management may inform future interventions to focus on how weight-related conversations with friends is related to overweight perception. More research about the relationship between weight-related conversations had with friends, self-perception of weight, and health behaviors is needed to confirm these findings. / Dissertation/Thesis / Masters Thesis Nutrition 2016

abstract: Many models of colonial interaction are build from cases of European colonialism among Native American and African peoples, and, as a result, they are often ill-suited to account for state expansion and decline in non-Western contexts. This dissertation investigates social organization and intraregional interaction in a non-western colonial context to broaden understanding of colonial interaction in diverse sociocultural settings. Drawing on social identity theory, population genetics, and social network analysis, patterns of social organization at the margins of the expansive pre-Hispanic Tiwanaku state (ca. AD 500-1100) are examined. According to the dual diaspora model of Tiwanaku colonial organization in the Moquegua Valley of southern Peru, Chen Chen-style and Omo-style ethnic communities who colonized the valley maintained distinct ethnic identities in part through endogamous marriage practices. Biodistance analysis of cranial shape data is used to evaluate regional gene flow among Tiwanaku-affiliated communities in Moquegua. Overall, results of biodistance analysis are consistent with the dual diaspora model. Omo- and Chen Chen-style communities are distinct in mean cranial shape, and it appears that ethnic identity structured gene flow between ethnic groups. However, there are notable exceptions to the overall pattern, and it appears that marriage practices were structured by multiple factors, including ethnic affiliation, geographic proximity, and smaller scales of social organization, such as corporate kin groups. Social network analysis of cranial shape data is used to implement a multi- and mesoscalar approach to social organization to assess family-based organization at a regional level. Results indicate the study sample constituted a social network comprised of a dense main component and a number of isolated actors. Formal approaches for identifying potential family groups (i.e., subgroup analysis) proved more effective than informal approaches. While there is no clear partition of the network into distinct subgroups that could represent extended kin networks or biological lineages, there is a cluster of closely related individuals at the core of the network who integrate a web of less-closely related actors. Subgroup analysis yielded similar results as agglomerative hierarchical cluster analysis, which suggests there is potential for social network analysis to contribute to bioarchaeological studies of social organization and bioarchaeological research in general. / Dissertation/Thesis / Doctoral Dissertation Anthropology 2016

abstract: Due to vast resources brought by social media services, social data mining has received increasing attention in recent years. The availability of sheer amounts of user-generated data presents data scientists both opportunities and challenges. Opportunities are presented with additional data sources. The abundant link information in social networks could provide another rich source in deriving implicit information for social data mining. However, the vast majority of existing studies overwhelmingly focus on positive links between users while negative links are also prevailing in real- world social networks such as distrust relations in Epinions and foe links in Slashdot. Though recent studies show that negative links have some added value over positive links, it is dicult to directly employ them because of its distinct characteristics from positive interactions. Another challenge is that label information is rather limited in social media as the labeling process requires human attention and may be very expensive. Hence, alternative criteria are needed to guide the learning process for many tasks such as feature selection and sentiment analysis. To address above-mentioned issues, I study two novel problems for signed social networks mining, (1) unsupervised feature selection in signed social networks; and (2) unsupervised sentiment analysis with signed social networks. To tackle the first problem, I propose a novel unsupervised feature selection framework SignedFS. In particular, I model positive and negative links simultaneously for user preference learning, and then embed the user preference learning into feature selection. To study the second problem, I incorporate explicit sentiment signals in textual terms and implicit sentiment signals from signed social networks into a coherent model Signed- Senti. Empirical experiments on real-world datasets corroborate the effectiveness of these two frameworks on the tasks of feature selection and sentiment analysis. / Dissertation/Thesis / Masters Thesis Computer Science 2017

abstract: With the rise of Online Social Networks (OSN) in the last decade, social network analysis has become a crucial research topic. The OSN graphs have unique properties that distinguish them from other types of graphs. In this thesis, five month Tweet corpus collected from Bangladesh - between June 2016 and October 2016 is analyzed, in order to detect accounts that belong to groups. These groups consist of official and non-official twitter handles of political organizations and NGOs in Bangladesh. A set of network, temporal, spatial and behavioral features are proposed to discriminate between accounts belonging to individual twitter users, news, groups and organization leaders. Finally, the experimental results are presented and a subset of relevant features is identified that lead to a generalizable model. Detection of tiny number of groups from large network is achieved with 0.8 precision, 0.75 recall and 0.77 F1 score. The domain independent network and behavioral features and models developed here are suitable for solving twitter account classification problem in any context. / Dissertation/Thesis / Masters Thesis Computer Science 2017

abstract: Our ability to understand networks is important to many applications, from the analysis and modeling of biological networks to analyzing social networks. Unveiling network dynamics allows us to make predictions and decisions. Moreover, network dynamics models have inspired new ideas for computational methods involving multi-agent cooperation, offering effective solutions for optimization tasks. This dissertation presents new theoretical results on network inference and multi-agent optimization, split into two parts - The first part deals with modeling and identification of network dynamics. I study two types of network dynamics arising from social and gene networks. Based on the network dynamics, the proposed network identification method works like a `network RADAR', meaning that interaction strengths between agents are inferred by injecting `signal' into the network and observing the resultant reverberation. In social networks, this is accomplished by stubborn agents whose opinions do not change throughout a discussion. In gene networks, genes are suppressed to create desired perturbations. The steady-states under these perturbations are characterized. In contrast to the common assumption of full rank input, I take a laxer assumption where low-rank input is used, to better model the empirical network data. Importantly, a network is proven to be identifiable from low rank data of rank that grows proportional to the network's sparsity. The proposed method is applied to synthetic and empirical data, and is shown to offer superior performance compared to prior work. The second part is concerned with algorithms on networks. I develop three consensus-based algorithms for multi-agent optimization. The first method is a decentralized Frank-Wolfe (DeFW) algorithm. The main advantage of DeFW lies on its projection-free nature, where we can replace the costly projection step in traditional algorithms by a low-cost linear optimization step. I prove the convergence rates of DeFW for convex and non-convex problems. I also develop two consensus-based alternating optimization algorithms --- one for least square problems and one for non-convex problems. These algorithms exploit the problem structure for faster convergence and their efficacy is demonstrated by numerical simulations. I conclude this dissertation by describing future research directions. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017