The thesis focuses on the social web and on the analysis of social networks with particular emphasis on their temporal aspects. Social networks are represented here by Time Varying Graphs (TVG), a general model for dynamic graphs borrowed from distributed computing. In the first part of the thesis we focus on the temporal aspects of social networks. We develop various temporal centrality measures for TVGs including betweenness, closeness, and eigenvector centralities, which are well known in the context of static graphs. Unfortunately the computational complexities of these temporal centrality metrics are not comparable with their static counterparts. For example, the computation of betweenness becomes intractable in the dynamic setting. For this reason, approximation techniques will also be considered. We apply these temporal measures to two very different datasets, one in the context of knowledge mobilization in a small community of university researchers, the other in the context of Facebook commenting activities among a large number of web users. In both settings, we perform a temporal analysis so to understand the importance of the temporal factors in the dynamics of those networks and to detect nodes that act as “accelerators”. In the second part of the thesis, we focus on a more standard static graph representation. We conduct a propagation study on YouTube datasets to understand and compare the propagation dynamics of two different types of users: subscribers and friends. Finally, we conclude the thesis with the proposal of a general framework to present, in a comprehensive model, the influence of the social web on e-commerce decision making.

Centrality measures in network analysis have become a popular measurement tool for identifying coherent nodes within a network. In the context of stock markets, the centrality measure helps to identify key performing ele- ments and strengths for specific stocks and determine their impact on disrupting market value and performance. Multiple studies presented practical implementations of centrality measures for determining trends and perform- ance of a particular market. However, fewer studies applied centrality measures to predict trends in the stock market.

The contemporary era is witnessing a systemic transition in the Canadian science and research paradigm. The research world is shrinking rapidly in response to modern technological developments, commercial and regulatory integration, faster communications and transportation and proactive science, technology and innovation policy. It is increasingly challenging to make competitive progress in world-class innovation or to gain global leadership in science. Big-science is now proposed as one of the means to realize national innovation goals and international competitiveness. As a result, government support for large-scale innovation projects has increased multifold. This dissertation examines a range of hypotheses large-scale research projects enhance investigator exchanges and generate social capital that has significant downstream benefits, which would provide a reason to support big science beyond the instrumental goals of the projects themselves. Taking Genome Canada as an example, this dissertation examines the production and role of social capital generated through large-scale research projects to assess the evidence base for funding big science research. A group of 139 investigators who raised capital in the Genome Canada Applied Bioproducts and Crops (ABC) Competition in 2009 are examined in the context of their engagements and networks in 2000-2009 in four relational arenas, namely their area of expertise, institutional connections, research grants, and co-publications. The investigation reveals three main findings. First, large-scale innovation projects as delivered through Genome Canada, comply with the fundamentals of contemporary innovation network theory. Second, the ties amongst investigators generate social capital, which offers positional advantage and differential superior access to networked resources. Third, the social capital generated in actor relations has pronounced long term impacts on downstream research success. Inter-disciplinary and cross-institutional large-scale research projects that have strong elements of knowledge production and financial exchange are found to assist the federal government in advancing research and innovation objectives. The results of the current investigation provide a strong rationale for the integration of people, disciplines, and institutions under the umbrella of large-scale genomics and proteomics research, and possible lessons for other research fields.

In this thesis a known heuristic for decreasing a node's centrality scores while maintaining influence, called ROAM, is compared to a modified version specifically designed to decrease eigenvector centrality. The performances of these heuristics are also tested against the Shapley values of a cooperative game played over the considered network, where the game is such that influential nodes receive higher Shapley values. The modified heuristic performed at least as good as the original ROAM, and in some instances even better (especially when the terrorist network behind the World Trade Center attacks was considered). Both heuristics increased the influence score for a given targeted node when applied consecutively on the WTC network, and consequently the Shapley values increased as well. Therefore the Shapley value of the game considered in this thesis seems to be well suited for discovering individuals that are assumed to actively trying to evade social network analysis.

Methicillin-resistant Staphylococcus aureus (MRSA) is an antibiotic-resistant bacterium of epidemiologic importance in Canadian healthcare facilities. The contact between MRSA colonized or infected patients with other patients, healthcare workers (HCWs) and/or the healthcare environment can result in MRSA transmission and healthcare-associated MRSA (HA-MRSA) infections in hospitals. These HA-MRSA infections are linked with increased length of hospital stay, economic burden, morbidity and mortality. Although infection prevention and control programs initiated in 2009 in Canada and other developed countries (e.g., UK, France, Belgium, Denmark, etc.) have been relatively successful in reducing the rate of HA-MRSA infections, they continue to pose a threat to patients, especially to the more vulnerable in long term care and geriatric institutions. Historically, MRSA was a problem mainly in hospital settings but after mid-1990s new strains of MRSA have been identified among people without healthcare-related risks and have been classified as community-associated MRSA (CA-MRSA). Furthermore, the distinction between HA-MRSA and CA-MRSA strains is gradually waning due to both the introduction of HA-MRSA in communities, and the emergence of CA-MRSA strains in hospitals. The purpose of this thesis was to explore the feasibility of constructing healthcare networks to evaluate the role of healthcare providers (e.g., physicians) and places (e.g., patient rooms) in the transmission of MRSA in a large tertiary care facility. Method of investigation: a secondary data case-control study, using individual characteristics and network structure measures, conducted at The Ottawa Hospital (TOH) between April 1st, 2013 and March 31th, 2014. Results: It was feasible to build social networks in a large tertiary care facility based on electronic medical records data. The networks' size (represented by the number of vertices and lines) increased during the outbreak period (period 1) compared to the pre-outbreak period (period 0) for both groups and at all three TOH campuses. The calculated median degree centrality showed significant increase in value for both study groups during period 1 compared to period 0 for two of the TOH campuses (Civic and General). There was no significant difference between the median degree centrality calculated for each study group at the Heart Institute when compared for the two reference periods. The median degree centrality of the MRSA case group for period 0 showed no significant difference when compared to the same measure determined for the control group for all three TOH campuses. However, the median degree centrality calculated for period 1 was significantly increased for the control group compared to the MRSA case group for two TOH campuses (Civic and General) but showed no significant difference between the two groups from the Heart Institute. In addition, there was a correlation between the two network measures (degree centrality and eigenvector centrality) calculated to determine the most influential person or place in the MRSA case group networks. However, there was no correlation between the two network’s measures calculated for physicians included in MRSA case group networks. Conclusions: It is feasible to use social network analysis as an epidemiologic analysis tool to characterize the MRSA transmission in a hospital setting. The network's visible changes between the groups and reference periods were reflected by the network measures and supported also by known hospital patient movements after the outbreak onset. Furthermore, we were able to identify potential source cases and places just prior of the outbreak start. Unfortunately, we were not able to show the role of healthcare workers in MRSA transmission in a hospital setting due to limitations in data collection and network measure chosen (eigenvector centrality). Further research is required to confirm these study findings.