Social network analysis was used in combination with qualitative methods to identify characteristics of stakeholder networks that supported cases of institutional development in rural water sectors in Ghana, Malawi, India, Tajikistan, and Bolivia. Institutions studied included local governments, a national government institution, and community operator committees managing water treatment facilities. Interviews with 162 participants in these countries used a facilitated network drawing exercise to capture data on stakeholder relationships and perceptions of factors supporting institutional development. Quantitative analysis of these networks and qualitative analysis of perceived factors identified three network characteristics as supporting institutional development for rural water supply in multiple countries and types of institutions. The three characteristics are: information and skill ties between an institution and stakeholders at lower levels of sector hierarchy; information and skill ties between an institution and stakeholders at higher levels of sector hierarchy; and coordination between stakeholders at higher levels of sector hierarchy that strongly engage an institution. These three characteristics can be observed from a network perspective, and qualitative descriptions of these interactions can improve understanding of the nuance and benefit of particular network ties. Social network analysis on its own cannot predict whether an institution will develop if these network characteristics exist, but it can be used to identify where network ties are absent or weakly developed. Methods and findings from this research enable a rigorous analysis of complex stakeholder interactions in rural water sectors to identify where particular relationships might be strengthened, and strengthening the environments that support institutional development has the potential to lead to the stronger institutions that are necessary for sustainable rural water service delivery.

Next-generation sequencing (NGS) technologies have revolutionised research into nature and diversity of genomes and transcriptomes. Since the initial description of these technology platforms over a decade ago, massively parallel RNA sequencing (RNA-seq) has driven many advances in the characterization and quantification of transcriptomes. RNA-seq is a powerful gene expression profiling technology enabling transcript discovery and provides a far more precise measure of the levels of transcripts and their isoforms than other methods e.g. microarray. However, the analysis of RNA-seq data remains a significant challenge for many biologists. The data generated is large and the tools for its assembly, analysis and visualisation are still under development. Assemblies of reads can be inspected using tools such as the Integrative Genomics Viewer (IGV) where visualisation of results involves ‘stacking’ the reads onto a reference genome. Whilst sufficient for many needs, when the underlying variance of the genome or transcript assemblies is complex, this visualisation method can be limiting; errors in assembly can be difficult to spot and visualisation of splicing events may be challenging. Data visualisation is increasingly recognised as an essential component of genomic and transcriptomic data analysis, enabling large and complex datasets to be better understood. An approach that has been gaining traction in biological research is based on the application of network visualisation and analysis methods. Networks consist of nodes connected by edges (lines), where nodes usually represent an entity and edge a relationship between them. These are now widely used for plotting experimentally or computationally derived relationships between genes and proteins. The overall aim of this PhD project was to explore the use of network-based visualisation in the analysis and interpretation of RNA-seq data. In chapter 2, I describe the development of a data pipeline that has been designed to go from ‘raw’ RNA-seq data to a file format which supports data visualisation as a ‘DNA assembly graph’. In DNA assembly graphs, nodes represent sequence reads and edges denote a homology between reads above a defined threshold. Following the mapping of reads to a reference sequence and defining which reads a map to a given loci, pairwise sequence alignments are performed between reads using MegaBLAST. This provides a weighted similarity score that is used to define edges between reads. Visualisation of the resulting networks is then carried out using BioLayout Express3D that can render large networks in 3-D, thereby allowing a better appreciation of the often-complex network structure. This pipeline has formed the basis for my subsequent work on the exploring and analysing alternative splicing in human RNA-seq data. In the second half of this chapter, I provide a series of tutorials aimed at different types of users allowing them to perform such analyses. The first tutorial is aimed at computational novices who might want to generate networks using a web-browser and pre-prepared data. Other tutorials are designed for use by more advanced users who can access the code for the pipeline through GitHub or via an Amazon Machine Image (AMI). In chapter 3, the utility of network-based visualisations of RNA-seq data is explored using data processed through the pipeline described in Chapter 2. The aim of the work described in this chapter was to better understand the basic principles and challenges associated with network visualisation of RNA-seq data, in particular how it could be used to visualise transcript structure and splice-variation. These analyses were performed on data generated from four samples of human fibroblasts taken at different time points during their entry into cell division. One of the first challenges encountered was the fact that the existing network layout algorithm (Fruchterman- Reingold) implemented within BioLayout Express3D did not result in an optimal layout of the unusual graph structures produced by these analyses. Following the implementation of the more advanced layout algorithm FMMM within the tool, network structure could be far better appreciated. Using this layout method, the majority of genes sequenced to an adequate depth assemble into networks with a linear ‘corkscrew’ appearance and when representing single isoform transcripts add little to existing views of these data. However, in a small number of cases (~5%), the networks generated from transcripts expressed in human fibroblasts possess more complex structures, with ‘loops’, ‘knots’ and multiple ends being observed. In a majority of cases examined, these loops were associated with alternative splicing events, a fact confirmed by RT-PCR analyses. Other DNA assembly networks representing the mRNAs for genes such as MKI67 showed knot-like structures, which was found to be due to the presence of repetitive sequence within an exon of the gene. In another case, CENPO the unusual structure observed was due to reads derived from an overlapping gene of ADCY3 gene present on the opposite strand with reads being wrongly mapped to CENPO. Finally, I explored the use of a network reduction strategy as an approach to visualising highly expressed genes such as GAPDH and TUBA1C. Having successfully demonstrated the utility of networks in analysing transcript isoforms in data derived from a single cell type I set out to explore its utility in analysing transcript variation in tissue data where multiple isoforms expressed by different cells within the tissue might be present in a given sample. In chapter 4, I explore the analysis of transcript variation in an RNA-seq dataset derived from human tissue. The first half of this chapter describes the quality control of these data again using a network-based approach but this time based the correlation in expression between genes and samples. Of the 95 samples derived from 27 human tissues, 77 passed the quality control. A network was constructed using a correlation threshold of r ≥ 0.9, which comprised 6,109 nodes (genes) and 1,091,477 edges (correlations) and clustered. Subsequently, the profile and gene content of each cluster was examined and enrichment of GO terms analysed. In the second half of this chapter, the aim was to detect and analyse alternative splicing events between different tissues using the rMATS tool. By using a false-discovery rate (FDR) cut-off of < 0.01, I found that in comparisons of brain vs. heart, brain vs. liver and heart vs. liver, the program reported 4,992, 4,804 and 3,990 splicing events, respectively. Of these events, only 78 splicing events (52 genes) with more than 50% of exon inclusion level and expression level more than FPKM 30. To further explore the sometimes-complex structure of transcripts diversity derived from tissue, RNAseq assembly networks for KLC1, SORBS2, GUK1, and TPM1 were explored. Each of these networks showed different types of alternative splicing events and it was sometimes difficult to determine the isoforms expressed between tissues using other approaches. For instance, there is an issue in visualising the read assembly of long genes such as KLC1 and SORBS2, using a Sashimi plots or even Vials, just because of the number of exons and the size of their genomic loci. In another case of GUK1, tissue-specific isoform expression was observed when a network of three tissues was combined. Arguably the most complex analysis is the network of TPM1 where the uniquification step was employed for this highly expressed gene. In chapter 5, I perform a usability testing for NGS Graph Generator web application and visualising RNA-seq assemblies as a network using BioLayout Express3D. This test was important to ensure that the application is well received and utilised by the user. / Almost all participants of this usability test agree that this application would encourage biologists to visualise and understand the alternative splicing together with existing tools. The participants agreed that Sashimi plots rather difficult to view and visualise and perhaps would lose something interesting features. However, there were also reviews of this application that need improvements such as the capability to analyse big network in a short time, side-by-side analysis of network with Sashimi plot and Ensembl. Additional information of the network would be necessary to improve the understanding of the alternative splicing. In conclusion, this work demonstrates the utility of network visualisation of RNAseq data, where the unusual structure of these networks can be used to identify issues in assembly, repetitive sequences within transcripts and splice variation. As such, this approach has the potential to significantly improve our understanding of transcript complexity. Overall, this thesis demonstrates that network-based visualisation provides a new and complementary approach to characterise alternative splicing from RNA-seq data and has the potential to be useful for the analysis and interpretation of other kinds of sequencing data.

Over recent decades, the stakeholder arena for urban flood management has become well recognised as being complex and dynamic. Various stakeholders are involved before, during and after a flooding event, all of which have different interests and demands. Therefore, an initial stakeholder identification and analysis stage is required before detailed stakeholder engagement strategies can be developed and employed. Drawing on urban flood management in Zhuji, a typical medium-sized city that has suffered urban flooding in China, this research project used a mixed-method research methodology within a single case-study approach to explore the current stakeholder arena for urban flood management in a medium-sized Chinese city. By combining stakeholder salience analysis with social network analysis, this study tries to create a more nuanced insight into the stakeholder arena, so that stakeholder participation in urban flood management can be improved. This thesis produces several findings. First, it provides empirical evidence to show that traditional one-dimensional stakeholder analysis methods such as the level of interest and influence; cooperation and competition; cooperation and threat; and stakeholder interest and power cannot provide an in-depth understanding of a complex and dynamic stakeholder arena, as exists for urban flood management. By way of contrast, the proposed stakeholder analysis approach, which combines both stakeholder salience and network analyses, can create a multi-dimensional understanding of urban flood management stakeholders and allows the initial problem space to be recast into a more detailed or nuanced understanding of the problems presented. This improved understanding of the stakeholder arena and the related problem space provides a more solid information foundation upon which new stakeholder and community engagement practices can be developed. Second, this thesis argues that the Mitchell et al. (1997) salience model experiences limitations in practice. Only five of the seven salience groups were identified in the present research project, with both the Dangerous and Demanding stakeholder groups missing. This indicates that the identification of urban flood management stakeholders in a medium-sized Chinese city is highly dependent on their legitimate claims. Third, the social network analysis used in this project not only explores the relationships between stakeholders, but also provides an opportunity to present other one-dimensional stakeholder attitudes. This enhancement of the data beyond one-dimensional visual representations to dynamic and interactive processes not only better assists policy-makers in developing new and improved engagement practices, it also allows engagement practitioners to educate stakeholders and interactively improve understanding of the situation among those stakeholders. This understanding, in turn, is assumed to facilitate collaborative problem solving.

Nesse artigo, comparamos, por meio da Análise de Redes, a dimensão institucional dos Sistemas Nacionais de Inteligência. Com base em fontes ostensivas sobre as agências de inteligência, entrevistas com especialistas de cada país e revisão bibliográfica, foi possíel compilar uma base de dados capaz de mapear as relações (arestas) de comunicação e autoridade entre os três tipos de atores coletivos (vértices): organizações governamentais de supervisão e direção, organizações colegiadas de coordenação e agências de inteligência. Por enquanto, a base de dados é composta de informações no formato de matrizes e grafos de 34 países com centenas de dados. Como resultado, estão sendo consuzidos estudos de casos sobre os países, assim como análises comparativas com amostras pequenas. Os estudos comparados estão sendo orientados de acordo com um determiando conjunto de paises ou de variáveis de interesse (centralidade de grau, centralidade de intermediação, centralização de grau e centralização de intermediação). Neste exercício em particular, os resultados obtidos indicam a distribuição de poder e as vulnerabilidades organizacionais no nível de países, permitindo comparações dentro e entre os Sistemas Nacionai de Inteligências do Brasil, Rússia, India, China e África do Sul (BRICS). / In this article we compare institutional dimensions of National Intelligence Systems using Network Analysis. Based upon open data on intelligence agencies, interviews with country expert scholars, and bibliographical review, we were able to compile a database allowing the mapping of authority and communication links (edges) between three types of collective actors (nodes), namely intelligence agencies, coordinating bodies, and central government. So far, the database comprises matrix and graph information for thirty-four countries each with hundreds of data points. As a result, case studies on specific countries, as well as small n comparative analyses are being conducted. Comparative studies are driven either by interest in clusters of countries or in variables of interest (degree centrality, betweenness centrality, degree centralization, and betweenness centralization). In this particular exercise, results obtained indicate power distributions and organizational vulnerabilities at country level, allowing for comparisons between and among the national intelligence systems of Brazil, Russia, India, China, and South Africa (BRICS).

While most scholars agree that revolution is linked to international confrontation and violence, we do not understand why some revolutions lead to long, drawn out conflicts while others are largely ignored. Part of the problem is due to improper methodology, which uses models that make independent and identically distributed assumptions and do not take the complex network of relations that states share into account. Using social network analysis, we devise a network theory of revolution and international conflict that incorporates the revolutionary state's status and relational ties within other states into the relationship between revolution and international conflict. We find that larger and more well-connected revolutionary states, particularly those integral to global alliance networks and possessing a larger share of global military capacity, are more likely to become involved in international conflict. We also find evidence of non-normality in conventional logit and poisson probability models, showing current methods of measurement of revolution and international conflict to be flawed.

Social support for people living with HIV in rural America remains a considerably understudied aspect of HIV/AIDS prevention. People living with HIV/AIDS (PLHA) require extensive support in order to remain in care, and reduce their viral suppression, and other disease complications. Without support, the likelihood that PLHA will refrain from or drop out of treatment options is gravely heightened, which consequently poses a significant threat for efforts to eliminate HIV as a public health issue. Using a mixed-method approach to Social Network Analysis, this study examines the principal role that social support plays in a person’s likelihood to adhere to care and consequently, attain viral suppression. Specifically, it looks at the roles of the family, friends, partners/spouses, and healthcare providers. The study also explores how social relations serve as mediators to stigma and discrimination, especially for disproportionate groups. Closely linked to social support availability is the perceived level of significance of the type of support that is available to the subjects. The study therefore goes further to explore the subjects’ perception of the support they receive (emotional, informational, and instrumental) and their satisfaction with it. This is imperative in that it sheds light on the role that the subjects’ social relations plays in their retention in care. This research again takes an interdisciplinary approach by exploring the contribution of both communication and health communication strategies to effect behavioral change. It contributes to research on HIV/AIDS health equity, and infectious disease management. It also contributes to efforts to identify strategies to control the spread of HIV by proposing efficient ways to optimize social support through the stages of the Care Continuum and consequently, facilitate an increase in the number of people who attain viral suppression. Keywords: Social Network Analysis; Social Support; Rural HIV; PLHA; Stigma.

This research examines information flow in online, interest-based networks to determine if existing models of information dissemination are adequate to describe the communication processes that occur in online publics. This study finds that a small number of primary influencers from within online communities are central to information collection, collation, and distribution in online, interest-based networks. This finding is inconsistent with one-step, two-step, and multi-step flow models, which privilege mass media as the central source of information. To more accurately depict online information flow in interest-based networks, this study introduces the radial model of information flow. Furthermore, the results of this study show that communication processes in online publics are best explained using a combination of the transmissive paradigm of communication, on which information flow models are based, and a ritual view of communication. This research also contributes to the ongoing development of the situational theory of publics by identifying organized publics as a key subgroup of active publics. Organized publics are networks of individuals within active publics who frequently and consistently communicate on a shared interest or concern. Organized publics form active online communication networks and prepare for advocacy related to a shared interest, making them of particular interest to public relations professionals. Using a case study approach, this dissertation uses online network analysis and qualitative cluster analysis to study the role of community influencers in information flow and cultural development within the online young adult cancer community. Instead of focusing exclusively on social media as channel for message dissemination, the results of this study indicate that successful relationship building can best by achieved by public relations practitioners who work to develop authentic presences in online communities. This research shows that embracing a participatory model of public relations that actively engages primary influencers in the planning and campaign implementation processes can promote authentic online presences.

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Nesse artigo, comparamos, por meio da Análise de Redes, a dimensão institucional dos Sistemas Nacionais de Inteligência. Com base em fontes ostensivas sobre as agências de inteligência, entrevistas com especialistas de cada país e revisão bibliográfica, foi possíel compilar uma base de dados capaz de mapear as relações (arestas) de comunicação e autoridade entre os três tipos de atores coletivos (vértices): organizações governamentais de supervisão e direção, organizações colegiadas de coordenação e agências de inteligência. Por enquanto, a base de dados é composta de informações no formato de matrizes e grafos de 34 países com centenas de dados. Como resultado, estão sendo consuzidos estudos de casos sobre os países, assim como análises comparativas com amostras pequenas. Os estudos comparados estão sendo orientados de acordo com um determiando conjunto de paises ou de variáveis de interesse (centralidade de grau, centralidade de intermediação, centralização de grau e centralização de intermediação). Neste exercício em particular, os resultados obtidos indicam a distribuição de poder e as vulnerabilidades organizacionais no nível de países, permitindo comparações dentro e entre os Sistemas Nacionai de Inteligências do Brasil, Rússia, India, China e África do Sul (BRICS). / In this article we compare institutional dimensions of National Intelligence Systems using Network Analysis. Based upon open data on intelligence agencies, interviews with country expert scholars, and bibliographical review, we were able to compile a database allowing the mapping of authority and communication links (edges) between three types of collective actors (nodes), namely intelligence agencies, coordinating bodies, and central government. So far, the database comprises matrix and graph information for thirty-four countries each with hundreds of data points. As a result, case studies on specific countries, as well as small n comparative analyses are being conducted. Comparative studies are driven either by interest in clusters of countries or in variables of interest (degree centrality, betweenness centrality, degree centralization, and betweenness centralization). In this particular exercise, results obtained indicate power distributions and organizational vulnerabilities at country level, allowing for comparisons between and among the national intelligence systems of Brazil, Russia, India, China, and South Africa (BRICS).

Nesse artigo, comparamos, por meio da Análise de Redes, a dimensão institucional dos Sistemas Nacionais de Inteligência. Com base em fontes ostensivas sobre as agências de inteligência, entrevistas com especialistas de cada país e revisão bibliográfica, foi possíel compilar uma base de dados capaz de mapear as relações (arestas) de comunicação e autoridade entre os três tipos de atores coletivos (vértices): organizações governamentais de supervisão e direção, organizações colegiadas de coordenação e agências de inteligência. Por enquanto, a base de dados é composta de informações no formato de matrizes e grafos de 34 países com centenas de dados. Como resultado, estão sendo consuzidos estudos de casos sobre os países, assim como análises comparativas com amostras pequenas. Os estudos comparados estão sendo orientados de acordo com um determiando conjunto de paises ou de variáveis de interesse (centralidade de grau, centralidade de intermediação, centralização de grau e centralização de intermediação). Neste exercício em particular, os resultados obtidos indicam a distribuição de poder e as vulnerabilidades organizacionais no nível de países, permitindo comparações dentro e entre os Sistemas Nacionai de Inteligências do Brasil, Rússia, India, China e África do Sul (BRICS). / In this article we compare institutional dimensions of National Intelligence Systems using Network Analysis. Based upon open data on intelligence agencies, interviews with country expert scholars, and bibliographical review, we were able to compile a database allowing the mapping of authority and communication links (edges) between three types of collective actors (nodes), namely intelligence agencies, coordinating bodies, and central government. So far, the database comprises matrix and graph information for thirty-four countries each with hundreds of data points. As a result, case studies on specific countries, as well as small n comparative analyses are being conducted. Comparative studies are driven either by interest in clusters of countries or in variables of interest (degree centrality, betweenness centrality, degree centralization, and betweenness centralization). In this particular exercise, results obtained indicate power distributions and organizational vulnerabilities at country level, allowing for comparisons between and among the national intelligence systems of Brazil, Russia, India, China, and South Africa (BRICS).