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

With advances in high-throughput measurement techniques, large-scale biological data, such as protein-protein interaction (PPI) data, gene expression data, gene-disease association data, cellular pathway data, and so on, have been and will continue to be produced. Those data contain insightful information for understanding the mechanisms of biological systems and have been proved useful for developing new methods in disease diagnosis, disease treatment and drug design. This study focuses on two main research topics: (1) identifying protein complexes and (2) identifying disease genes from biomolecular networks. Firstly, protein complexes are groups of proteins that interact with each other at the same time and place within living cells. They are molecular entities that carry out cellular processes. The identification of protein complexes plays a primary role for understanding the organization of proteins and the mechanisms of biological systems. Many previous algorithms are designed based on the assumption that protein complexes are densely connected sub-graphs in PPI networks. In this research, a dense sub-graph detection algorithm is first developed following this assumption by using clique seeds and graph entropy. Although the proposed algorithm generates a large number of reasonable predictions and its f-score is better than many previous algorithms, it still cannot identify many known protein complexes. After that, we analyze characteristics of known yeast protein complexes and find that not all of the complexes exhibit dense structures in PPI networks. Many of them have a star-like structure, which is a very special case of the core-attachment structure and it cannot be identified by many previous core-attachment-structure-based algorithms. To increase the prediction accuracy of protein complex identification, a multiple-topological-structure-based algorithm is proposed to identify protein complexes from PPI networks. Four single-topological-structure-based algorithms are first employed to detect raw predictions with clique, dense, core-attachment and star-like structures, respectively. A merging and trimming step is then adopted to generate final predictions based on topological information or GO annotations of predictions. A comprehensive review about the identification of protein complexes from static PPI networks to dynamic PPI networks is also given in this study. Secondly, genetic diseases often involve the dysfunction of multiple genes. Various types of evidence have shown that similar disease genes tend to lie close to one another in various biomolecular networks. The identification of disease genes via multiple data integration is indispensable towards the understanding of the genetic mechanisms of many genetic diseases. However, the number of known disease genes related to similar genetic diseases is often small. It is not easy to capture the intricate gene-disease associations from such a small number of known samples. Moreover, different kinds of biological data are heterogeneous and no widely acceptable criterion is available to standardize them to the same scale. In this study, a flexible and reliable multiple data integration algorithm is first proposed to identify disease genes based on the theory of Markov random fields (MRF) and the method of Bayesian analysis. A novel global-characteristic-based parameter estimation method and an improved Gibbs sampling strategy are introduced, such that the proposed algorithm has the capability to tune parameters of different data sources automatically. However, the Markovianity characteristic of the proposed algorithm means it only considers information of direct neighbors to formulate the relationship among genes, ignoring the contribution of indirect neighbors in biomolecular networks. To overcome this drawback, a kernel-based MRF algorithm is further proposed to take advantage of the global characteristics of biological data via graph kernels. The kernel-based MRF algorithm generates predictions better than many previous disease gene identification algorithms in terms of the area under the receiver operating characteristic curve (AUC score). However, it is very time-consuming, since the Gibbs sampling process of the algorithm has to maintain a long Markov chain for every single gene. Finally, to reduce the computational time of the MRF-based algorithm, a fast and high performance logistic-regression-based algorithm is developed for identifying disease genes from biomolecular networks. Numerical experiments show that the proposed algorithm outperforms many existing methods in terms of the AUC score and running time. To summarize, this study has developed several computational algorithms for identifying protein complexes and disease genes from biomolecular networks, respectively. These proposed algorithms are better than many other existing algorithms in the literature.

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

Australian Government policy has sought to decrease university reliance on federal support through the re-allocation of funding. Access to this pool of funding is based on teaching and learning performance and the subsequent comparison with similar education institutions. The concept of community has been promoted as a strategy for responding to these government demands whilst facilitating the student learning experience. Despite an intensive investment in strategic initiatives to enhance sense of community among the student cohort, there is a lack of scaleable evaluative measures to assess the overall effectiveness and accomplishment of intended outcomes. Contemporary methods for the assessment of community primarily rely on the establishment of pre-defined characteristics and the subsequent content analyses of communication artefacts to identify presence or absence. These studies are often small in sample size and limited in scalability and therefore the generalisation of research findings is impeded. This study aimed to examine the relationship between student sense of community (SOC) and communication interactions. To achieve this aim the study first developed a scaleable quantitative methodology that can be used to benchmark current pedagogical performance and guide future implemented practices relating to the establishment of a student community. The study juxtaposes an established scale of SOC with student online communication behaviours to identify potential relationships. In developing this methodology the study confirmed that the Classroom Community Scale (CCS) was a valid and robust instrument. The study incorporated a mixed methods paradigm to investigate the research questions. Quantitative data were derived from an online survey (N= 464), student online communication interactions and social network analyses. These data were further explored using more qualitative approaches such as content analyses of the discussion forum transcripts (n = 899) and student interviews (N = 4). The findings demonstrate that students and teaching units with greater frequencies of communication interactions possess stronger levels of SOC as determined by the CCS (R2 = .24, F = 14.98, p < .001; R2 = .83, F = 16.53, p < .01, respectively). A significant correlation was observed between discussion forum interaction types (learner-learner; learner-content; system) and SOC. Although learner-to-learner interactions demonstrated a positive correlation (r = .48, p < .05), system posts (isolated contributions) illustrated a negative correlation (r = - .50, p < .05). Quantity of discussion forum postings alone was not observed to be a significant indicator of SOC. Social network analyses demonstrated that the centrality measures closeness and degrees are positive predictors of an individual's reported SOC (t = 3.02 and t = 3.24, p < .001 respectively). In contrast, the centrality measure betweenness revealed a negative correlation (t = -3.86, p < .001). Discussion forum content analyses illustrated the fluid transition of discourse between social and learning oriented communities. Student interviews suggested that pre-existing external networks influence the type of support and information exchanges required and therefore, the degree of SOC experienced. The study also recognised that a key challenge in the implementation of data mining practices to monitor lead indicators of community lies in the notion of surveillance. This study examined the impact of technologically mediated modes of surveillance on student online behaviour. The findings demonstrate that students' unaware of the surveillance technologies operating within the institution modify their online behaviour more than their cognisant peers. The results of this study have implications for educational theory, practice, monitoring and evaluation. This research supports the development of a new model of community that illustrates the inter-relationships between student SOC and the education environment. Furthermore, the developed methodology demonstrates the capacity for cost effective data mining techniques to guide and evaluate implemented teaching and learning practices. Consequently, alignment with other theoretical constructs such as student satisfaction and engagement provides the institution with a lead indicator of teaching and learning performance. As the findings from this study illustrate the relationship between communication interactions and SOC, educators have the capacity to monitor communication trends and alter the teaching and learning practices to promote community among the student cohort in a just-in-time environment.

The last two decades have seen an increasing sophistication in the type of information systems employed by organizations. In particular we have seen the emergence of enterprise systems technology - advanced information technology specifically designed to integrate the vast majority of an organization's processes and data flows. As the characteristics of ES technology have encroached beyond individual user domains and have become integrated throughout organizations, user acceptance issues have also broadened beyond the individual unit of analysis. At the same time numerous examples can be found both in the trade press and academic literature of organizations wishing to use enterprise systems as a primary driver of widespread organizational change and restructuring. A fundamental premise of this study is that while it may be intuitively appealing to consider technology as a primary catalyst for organizational change, it neglects to acknowledge the presence of what is referred to as the &quoteduality of structure&quote (Giddens, 1993). Duality of structure proponents contend that while IT system protocols may to a certain extent determine individual action, human agency can also determine the extent to which the technology is incorporated into everyday operations. The failure of past research to acknowledge the role of individual action and the influence of social context in determining IT usage is considered to be a significant oversight (DeSanctis & Poole, 1994). Underpinned by the theory of structuration and its notion of duality, a theory of user acceptance is put forward capable of clarifying the process by which users evaluate and react to enterprise systems technology. The thesis reports on an empirical investigation into the relationship between three representations of structure within an organization: the characteristics of ES technology; job design; and social networks. The capacity of ES technology to alter the structural elements of both job design and social networks, and hence form user's attitudes and behavior towards the system, is the fundamental theoretical premise of the thesis. As such this represents a clear step forward in understanding the implications of ES technology for both users and organizational structure. Using a longitudinal embedded single case design, this study examines the user acceptance and structural implications of introducing an ES into a large public sector educational institution. A social network and job design perspective was adopted to offer fresh insight into the dynamics of employee reaction to the introduction of ES technology. Five hypotheses support the job design component of the thesis. It was argued that given the inherent design elements of ES technology, along with the specific intent of the system's introduction, that users would both anticipate and perceive a decrease in job characteristics following an ES implementation. Further, that the positive relationship between job change and user acceptance would be moderated by the amount of system usage reported by users. Users with a greater exposure to the system were hypothesized to have a far stronger relationship between job change and acceptance than low users. The ramifications of perceived or actual changes to embedded resource exchange networks and subsequent employee reactions to those changes were also considered. Essentially social networks were argued to play a dual role in the user acceptance process, one being a conduit for the facilitation and transfer of user attitudes towards new systems, the other acting as a catalyst for attitude formation towards new systems. Overall the findings only partially supported four of the eight hypotheses put forward. While users were seen to anticipate an &quoteacross the board&quote decrease in job characteristics at Time 1 following the introduction of an ES, perceived changes in job characteristics at Time 2 were dependant on user hierarchy and the extent of system usage. Those high in formal authority reported an increase in job enrichment following the system's introduction, while those low in formal authority reported a decrease in overall job enrichment. Usage was also seen to moderate the relationship between job change and user acceptance. At Time 1 low users reported a positive relationship between anticipated changes in meaningfulness and user acceptance. Conversely at Time 1 high users reported a negative relationship between anticipated skill variety levels at Time 2 and user acceptance. Only one job characteristic reported a relationship between usage and user acceptance. Low users reported a positive relationship between changes in task identity and user acceptance. A post-hoc profile of the usage categories indicated that high users were more likely to be a lower hierarchical position than low users. The positive relationship reported by low users at Time 1 and Time 2 was explained by both the nature of the system, as well as the type and quantity of information received by low users. As senior members of the organization they were considered more likely to receive information that highlighted its attributes in the context of their job roles. The inherent design of ES technology, along with the specific intent it was being introduced, facilitated largely management orientated objectives. Therefore it is unsurprising that low users anticipating an increase in experienced meaningfulness following the introduction of a system that enhanced their job role reported corresponding acceptance levels. In contrast, the negative relationship between anticipated levels of skill variety at Time 2 and perceived ease of use was explained by the affinity that high users were likely to have with the old system. To high users with a high degree of proficiency associated with a redundant skill set, increased skill variety only represented a steeper learning curve and an increased pressure to adapt to the new system. The network component of the study also produced mixed results. Of the two networks that were measured over time, only one supported the hypothesized increase in both advice and resource exchange networks over time. Post-hoc analyses indicated that two of the four groups exhibited network change consistent with the hypothesized relationship. Anecdotal reports suggested that contextual elements such as geographical location and managerial policy at a localized level determined the nature of the change for the remaining two groups. The results failed to support the relationship between network change and user acceptance. However, a weak but significant negative relationship between the measure of network efficiency and user acceptance was found. In simple terms users developing an increasingly redundant set of contacts reported higher levels of user acceptance. In sum, the thesis represents a contribution to enterprise systems, user acceptance and social network literatures. In the first instance the research validates the call by Orlikowski & Iacono (2001) to readily acknowledge the specific nature of the technology under investigation. Despite the growth and saturation of enterprise system types, comparatively little research has been undertaken to examine the user and organizational issues surrounding their implementation. This research has demonstrated the capacity for the inherent design elements of ES technology to have differential effects in terms of job design for different user classifications. This and other findings represent a step forward in understanding the structural and user acceptance implications of this technology, while sign-pointing a number of promising future research avenues. The job design results, and to a lesser extent the network efficiency results, demonstrate the effect of social context on user acceptance. As such they provide further insight regarding the potential determinants of user acceptance beyond the individual unit of analysis. The findings also indicate an increasing need for user acceptance research to stretch beyond the transitory, short term measures of user acceptance such as perceived ease of use, usefulness, training and computer efficacy. Finally the thesis contributes to a small, but growing literature examining the role of social networks in the process of organizational change. In particular this thesis has considered in detail, the attitudinal and behavioral consequences of artificially altering established patterns of interaction. As such the study highlights the need to better understand the role of networks not only in the case of facilitating change, but the effect of network change in terms of change intervention success.

Abstract Drawing on the serious leisure perspective, social world theory, and social network analysis this thesis utilizes an exploratory methodology to develop a structural view of a social world network of 49 social actors comprised of the grassroots associations and the allied organisations expressly concerned with amateur artists in a regional Australian city. Semistructured interviews were conducted with spokespeople in leadership and management roles with the associations and organisations. The purpose of the interviews was to develop an understanding of the key attributes of the grassroots associations and the function of the allied commercial, cultural, and educational organisations, and to discover the patterns of links between these two types of social actors. In addition, the interviews explored the types of social world participation among the associational memberships; and the role, rewards, and costs experienced by the spokespeople who were fulfilling coordinating duties in the grassroots associations. The research found that associations of amateur artists were active in the local community coordinating their memberships, activities, and assets to provide calendars of events for the participants in a regional social world of the creative arts and that, the allied organisations provided complementary goods and services. Further, it emerged that links of varying intensity connecting the associations and organisations coalesced into a network. This comprised a cluster of social actors connected by their concern with actors, dancers, and musicians; a cluster of social actors connected by their concern with craft practitioners, community cultural development workers, visual artists, and writers; and of social actors with bilateral links connecting the two clusters. Also mixed serious leisure emerged as a significant mode of participation among the sample of grassroots association spokespeople who were interviewed and this was important to the sustainability of their associations over time. There are three major outcomes from the research. First, structural concepts from social network analysis in combination with social world theory developed into definition of a community level serious leisure network; second, this definition proved empirically viable in the research context, and third, a model to depict the phenomenon of a community level serious leisure network has emerged from the exploratory process. The findings have both theoretical and empirical implications. Theoretically, they assist research into the structure of community level leisure provision. The findings also encourage investigation of mixed serious leisure. Empirically, the application of network knowledge to improve community leisure resources can improve the outcomes for the social actors involved and the community in which they are embedded.

Abstract Drawing on the serious leisure perspective, social world theory, and social network analysis this thesis utilizes an exploratory methodology to develop a structural view of a social world network of 49 social actors comprised of the grassroots associations and the allied organisations expressly concerned with amateur artists in a regional Australian city. Semistructured interviews were conducted with spokespeople in leadership and management roles with the associations and organisations. The purpose of the interviews was to develop an understanding of the key attributes of the grassroots associations and the function of the allied commercial, cultural, and educational organisations, and to discover the patterns of links between these two types of social actors. In addition, the interviews explored the types of social world participation among the associational memberships; and the role, rewards, and costs experienced by the spokespeople who were fulfilling coordinating duties in the grassroots associations. The research found that associations of amateur artists were active in the local community coordinating their memberships, activities, and assets to provide calendars of events for the participants in a regional social world of the creative arts and that, the allied organisations provided complementary goods and services. Further, it emerged that links of varying intensity connecting the associations and organisations coalesced into a network. This comprised a cluster of social actors connected by their concern with actors, dancers, and musicians; a cluster of social actors connected by their concern with craft practitioners, community cultural development workers, visual artists, and writers; and of social actors with bilateral links connecting the two clusters. Also mixed serious leisure emerged as a significant mode of participation among the sample of grassroots association spokespeople who were interviewed and this was important to the sustainability of their associations over time. There are three major outcomes from the research. First, structural concepts from social network analysis in combination with social world theory developed into definition of a community level serious leisure network; second, this definition proved empirically viable in the research context, and third, a model to depict the phenomenon of a community level serious leisure network has emerged from the exploratory process. The findings have both theoretical and empirical implications. Theoretically, they assist research into the structure of community level leisure provision. The findings also encourage investigation of mixed serious leisure. Empirically, the application of network knowledge to improve community leisure resources can improve the outcomes for the social actors involved and the community in which they are embedded.