Certain properties of individuals’ social networks within their organizations are known to be associated with benefits. However, these properties are not universally beneficial for all individuals. To explain the differing utility of social connections for different actors, network research has tended to focus on factors relating to the actor’s characteristics, agency and cognition. With this dissertation, I explore a different contingency affecting actors’ abilities to leverage their networks: how observers perceive and evaluate the behavior of actors as they craft and use their networks, and how these attributions impact actors’ job performance. I develop a theoretical framework that incorporates social capital theory to develop a taxonomy of networking behaviors. I build upon network cognition research to explore how observers’ perceptions and attributions of actors’ networking behaviors rather than perceptions of network ties or structure affect actors’ outcomes. I draw upon attribution theory to suggest how observers’ attributions about actors may affect observers’ behavior towards actors, thus impacting actors’ outcomes. Results suggest that networking behaviors that are seen as serving the collective positively impact actors’ outcomes, while networking behaviors that are seen as self-serving negatively impact the actors’ outcomes by limiting access to high-status friends. However, attributions about an actor’s self-serving behavior augment the benefits the actor receives when he or she has access to high-status friends. When it comes to performance, networks matter, but it also matters how observers evaluate actors’ networking behaviors.

This study was designed to explore the possible connections between student peer relationships and individual students’ roles in a network as it pertained to outcomes such as self-reported academic achievement and personal satisfaction with the first year of college. The research question directing this inquiry is: How does a student’s role within a residential community of peers relate to success in college? Social network analysis was employed for examination of individual engagement within the context of a larger community. The vast learning community literature tells an interesting story: 1.) a history of co-curricular peer learning environments, 2) a tradition of research intended to assess the value of these programs, 3) a body of literature that provides theoretical explanations for why learning communities should work. The gap in the literature is found regarding what happens within the communities. To learn how individuals within community learn from one another, community of practice was utilized as a framework in this mixed-methods approach to examine the influence of relationships, and exchange, acquisition, & development of social capital within a living learning community While this network study indicated that popularity, relational ties to staff, and being someone sought-after for advice were not statistically significant predictors of higher GPA, the network analyses confirmed strong network density, cohesion, and proper structure for ideal capital flow. The results of this study confirm that this community is effective in establishing familiarity and even more so, providing an environment that fosters friendships among participants and staff. Furthermore, students developed the ability to construct knowledge alongside their peers. Given the density and relation-rich nature of this community, this positive environment is able to foster more complex and self-authored levels of meaning-making for the students involved. Building this scaffolding facilitated student development, which effectively created a student transformation from dependence on external authority to self-authorship. This study confirmed that the primary goals of a learning community have been met: a group of strangers developed into a network of friends who reap social and academic benefits by virtue of being together in a shared and successful living learning community environment.

This research presents a structural model of the effect of the organization of military units upon their capability. This research is oriented towards a more complete understanding of military capability and policy decisions about the structure and development of military forces. We identify the types of national and military policy decisions that claims of military capability inform, and find that there are five distinct types of capability claims relevant to military policy. We show how these types of capability claims are logically related to each other, but have different premises, predicates, and standards of proof. We find that one of these types of claims, General Organization Capability Claims, ties together the various military policy decisions. The remainder of this research shows how these capability claims can be formally structured based on military doctrine and structurally evaluated using a network-science based model. The interaction between the structural elements of a military organization (personnel, materiel, and information) and the things it is supposed to do (military tasks) can be represented and analyzed with network science methods, and represents a type of general organization capability claim. We present a method for representing policy decisions about unit structure and tactical doctrine. We then develop two versions of a structural model of capability–one that links the individual elements of an organization to the tasks it performs; another that considers the capacity of a set of organizations to meet a set of requirements. We show that network statistics of organizations represented off of authoritative, rather than observational, data are still consistent with network science findings but require interpretation. We also show how alternate methods of aggregating organizations can expand the utility of the capability measurement. This research presents five new contributions to the fields of military policy analysis and network science–(1) a taxonomy of military capability claims, (2) a meta-network model of doctrinal organization and task data, (3) a structural model of organization capability, (4) a structural model of organization capacity, and (5) a network-based method integer programming method.

The sharing of needles among injection drug users (IDUs) is a common route of Human Immunodeficiency Virus and Hepatitis C Virus transmission. Through the increased utilization of social network analysis, researchers have been able to examine how the interpersonal relationships of IDUs affect injection risk behaviour. This study involves a secondary analysis of data from a cross-sectional study of 156 IDUs from Winnipeg, Manitoba titled “Social Network Analysis of Injection Drug Users”. Multiple logistic regression analysis was used to assess the individual and the social network characteristics associated with needle sharing among the IDUs. Generalized Estimating Equations analysis was used to determine the injecting dyad characteristics which influence needle sharing behaviour between the IDUs and their injection drug using network members. The results revealed five key thematic findings that were significantly associated with needle sharing: (1) types of drug use, (2) socio-demographic status, (3) injecting in semi-public locations, (4) intimacy, and (5) social influence. The findings from this study suggest that comprehensive prevention approaches that target individuals and their network relationships may be necessary for sustainable reductions in needle sharing among IDUs.

Sharing and inappropriate discarding of syringes and drug use equipment can lead to transmission of bloodborne pathogens and decreased sense of community safety. To reduce these risks, interventions such as syringe drop boxes, are implemented. However, little consideration has been made of the social and spatial networks of the injection drug use (IDU) populations in the placement of these drop boxes. A sample of IDU was obtained through respondent driven sampling in Winnipeg, Canada in 2009. Characteristics of the sample and distribution of these characteristics through the social network were assessed. A spatial network was constructed which focused on the connections between IDU and specific geographic locations. Measures of centrality were calculated using Pajek and the geographic network was mapped using ArcGIS. Analysis of the social network revealed variation among network components in demographic and drug use characteristics. Spatial analysis revealed geographic clustering, quantified through network centrality measures. There was congruence between locations of high degree and current drop box placement in Winnipeg. This research illustrates the benefit of combining IDU social network and spatial data to inform evidence-based municipal policies and programs.

Coordination of people, processes, and artifacts is a significant challenge to successful software engineering that is growing as the scale, distribution, and complexity of software projects grow. This thesis presents an exploratory case study of coordination of interdependent work in a practicing software development team. Qualitative analysis of stakeholder interviews was used to develop nine theoretical propositions that describe coordination behaviours. One proposition was refined by quantitatively exploring the structure of explicit dependencies between work items in relation to their resolution times. Structure measures drawn from social network analysis were used to quantify the structure of explicit dependencies between work items, revealing some lower resolution times were associated with degree centrality measures, but that network structures only explain a small proportion of the variance in resolution times. The results are compared with existing theories of coordination in software engineering and directions for further research are outlined.

Proteins participate in the majority of cellular processes. To determine the function of a protein it is not sufficient to solely know its sequence, its structure in isolation, or how it works individually. Additionally, we need to know how the protein interacts with other proteins in biological networks. This is because most of the proteins perform their main function through interactions. This thesis sets out to improve the understanding of protein-protein interaction networks (PPINs). For this, we propose three approaches: (1) Studying measures and methods used in social and complex networks. The methods, measures, and properties of social networks allow us to gain an understanding of PPINs via the comparison of different types of network families. We studied models that describe social networks to see which models are useful in describing biological networks. We investigate the similarities and differences in terms of the network community profile and centrality measures. (2) Studying PPINs and their role in evolution. We are interested in the relationship of PPINs and the evolutionary changes between species. We investigate whether the centrality measures are correlated with the variability and similarity in orthologous proteins. (3) Studying protein features that are important to evaluate, classify, and predict interactions. Interactions can be classified according to different characteristics. One characteristic is the energy (that is the attraction or repulsion of the molecules) that occurs in interacting proteins. We identify which type of energy values contributes better to predicting PPIs. We argue that the number of energetic features and their contribution to the interactions can be a key factor in predicting transient and permanent interactions. Contributions of this thesis include: (1) We identified the best community sizes in PPINs. This finding will help to identify important groups of interacting proteins in order to better understand their particular interactions. We furthermore find that the generative model describing biological networks is very different from the model describing social networks A generative model is a model for randomly generating observable data. We showed that the best community size for PPINs is around ten, different from the best community size for social and complex network (around 100). We revealed differences in terms of the network community profile and correlations of centrality measures; (2) We outline a method to test correlation of centrality measures with the percentage of sequence similarity and evolutionary rate for orthologous proteins. We conjecture that a strong correlation exists. While not obtaining positive results for our data. Therefore, (3) we investigate a method to discriminate energetic features of protein interactions that in turn will improve the PPIN data. The use of multiple data sets makes possible to identify the energy values that are useful to classify interactions. For each data set, we performed Random Forest and Support Vector Machine with linear, polynomial, radial, and sigmoid kernels. The accuracy obtained in this analysis reinforces the idea that energetic features in the protein interface help to discriminate between transient and permanent interactions. / Graduate / 0984

New social networking and social web tools are becoming available and are easing the process of customizing online social environments. With these developments in technology, core design efforts are being extended beyond usability for individual users and beginning to include notions of sociability for the engagement of communities of users. This thesis is an investigation of these developments. It is guided by the principal research question: how do you design for social engagement in an online social environment intended to facilitate interaction in a community of users? To address this question, this thesis presents a domain-community model developed from the communities of practice concept and the Work Domain Analysis model used in Cognitive Work Analysis. The domain-community model provides a basis for the design a composition of web components for an online social environment that will addresses issues of social engagement and domain effectiveness. In a case study, the domain-community model was used as a basis for the redesign of a social networking portal used by an international development leadership community called UCP-SARnet. A social network analysis of core members of UCP-SARnet was conducted before and after the portal was redesigned. From the social network analysis, it was concluded that the structure of UCP-SARnet was positively affected by the redesign: core group members reported they knew one another significantly more after the redesign of the website than before the redesign. User experience measures of the UCP-SARnet portal, website usage data, and a tally of website communication activity also changed significantly with the redesign of the website. This provided more evidence that a design informed by Cognitive Work Analysis and communities of practice produced a measurable effect on the structure of the UCP-SARnet online community. As such, this model can provide a basis for designers of online communities to more systematically account for social phenomena in relation to collective efforts in a given work domain. Furthermore, it is expected the effectiveness of the model can be taken forward with future work by refining the domain-community model, developing techniques to translate the model into interface concepts, and building practices for community-based research and design.

The sharing of needles among injection drug users (IDUs) is a common route of Human Immunodeficiency Virus and Hepatitis C Virus transmission. Through the increased utilization of social network analysis, researchers have been able to examine how the interpersonal relationships of IDUs affect injection risk behaviour. This study involves a secondary analysis of data from a cross-sectional study of 156 IDUs from Winnipeg, Manitoba titled “Social Network Analysis of Injection Drug Users”. Multiple logistic regression analysis was used to assess the individual and the social network characteristics associated with needle sharing among the IDUs. Generalized Estimating Equations analysis was used to determine the injecting dyad characteristics which influence needle sharing behaviour between the IDUs and their injection drug using network members. The results revealed five key thematic findings that were significantly associated with needle sharing: (1) types of drug use, (2) socio-demographic status, (3) injecting in semi-public locations, (4) intimacy, and (5) social influence. The findings from this study suggest that comprehensive prevention approaches that target individuals and their network relationships may be necessary for sustainable reductions in needle sharing among IDUs.

Uganda has a predominantly young population, and there is a need for targeted HIV/AIDS prevention programming. Peer education is a health intervention style that has been used with appreciable success in adolescent groups, but some issues exist. We hypothesize that more can be done in the program planning stages to increase the chances of sustained success, and have completed two different types of cross-sectional analyses to investigate this aspect. We used Social Network Analysis to examine the social structure of two secondary schools in Fort Portal, Uganda. We identified existing modes of influence and natural channels of communication, and used these to create a feasible model of peer educator selection. We also studied present levels of communication about sexual and reproductive health within youth relationships, and found that youth are willing to talk to their friends, but high levels of communication do not generally occur. This provides an important point of entry for health promotion programs. / Population Health