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Grayson Baker
Grayson Baker

Social Network Analysis Pdf Download

Book DescriptionThe revised and updated edition of this bestselling text provides an accessible introduction to thetheory and practice of network analysis in the social sciences. It gives a clear and authoritativeguide to the general framework of network analysis, explaining the basic concepts, technicalmeasures and reviewing the available computer programs. The book outlines both the theoreticalbasis of network analysis and the key techniques for using it as a research tool. Building upondefinitions of points, lines and paths, John Scott demonstrates their use in clarifying suchmeasures as density, fragmentation and centralization. He identifies the various cliques,components and circles into which networks are formed, and outlines

Social Network Analysis Pdf Download

Social Network Analysis and Mining (SNAM) is a multidisciplinary journal serving researchers and practitioners in academia and industry. It is the main venue for a wide range of researchers and readers from computer science, network science, social sciences, mathematical sciences, medical and biological sciences, financial, management and political sciences. We solicit experimental and theoretical work on social network analysis and mining using a wide range of techniques from social sciences, mathematics, statistics, physics, network science and computer science.

Artificial intelligence (AI) is changing the landscape of healthcare and modern personalized precision medicine. With the increasing availability of healthcare data and rapid progress of machine learning algorithms, AI and big data analytics are enabling unprecedented analysis of healthcare. Social media networks, when combined with Big Data applications and health policymaking, enable the development of smart public health applications that will result in high-quality health delivery and reduced costs.

Social network analysis attracts increasing attention in economic geography. We claim social network analysis is a promising tool for empirically investigating the structure and evolution of inter-organizational interaction and knowledge flows within and across regions. However, the potential of the application of network methodology to regional issues is far from exhausted. The aim of our paper is twofold. The first objective is to shed light on the untapped potential of social network analysis techniques in economic geography: we set out some theoretical challenges concerning the static and dynamic analysis of networks in geography. Basically, we claim that network analysis has a huge potential to enrich the literature on clusters, regional innovation systems and knowledge spillovers. The second objective is to describe how these challenges can be met through the application of network analysis techniques, using primary (survey) and secondary (patent) data. We argue that the choice between these two types of data has strong implications for the type of research questions that can be dealt with in economic geography, such as the feasibility of dynamic network analysis.

Social network analysis method for SARS-CoV-2 contact tracing data would be of use in measuring individual patient level variations in disease transmission. The network metrics identified individual patients and patient components who have disproportionately contributed to transmission. The network measures and graphical tools could complement the existing contact tracing indicators and could help improve the contact tracing activities.

While SARS-CoV-2 contact tracing data is continuously collected from hundreds of thousands of SARS-CoV-2 patients, it has not been explored for understanding patient-level heterogeneity using suitable analysis methods. While standard epidemiological analysis methods are in place to generate key parameters of the pandemic, still the suitability of social network analysis methods to assess the transmission heterogeneity has not been attempted partly due to lack of appropriate contact tracing data. Assessing the individual level of variations would require a relational dataset indicative of all contact ties which have occurred between patients and their contacts.

Experiences from past have highlighted the utility of social network analysis methods in successfully exploring individual-level transmission events of infectious diseases like Severe Acute Respiratory Syndrome(SARS), Tuberculosis (TB) and sexually transmitted infections (STIs) [11,12,13,14,15]. Network analysis methods involving quantitative metrics and sociograms could be appropriate and would help to easily comprehend the transmission events at the granular level, i.e. individuals [16, 17]. In this background, we propose to adopt social network analysis methods to assess and understand the heterogeneity of SARS-CoV2 transmission at individual patients level.

The present analysis calculated social network centrality measures to identify the key nodal patients who were influential in transmitting the SARS-CoV-2 infection. Components analysis was conducted to identify patient sub-network structures which have disproportionately contributed to infection transmission. Average path length and network diameter measures were calculated to assess the dispersion level of infection within patient networks. A graphical explanation of the following social metrics are provided in Fig. 1.

Epidemiological studies on SARS-CoV-2 have emphasized the importance of heterogeneity in transmission and the need for measuring transmission events and variations at the individual levels [7,8,9,10]. These studies have emphasized the importance of heterogeneity information in addition to estimating basic reproduction number. Through this study we have attempted to adopt the standard social network analysis methods to measure heterogeneity by analyzing a large scale contact tracing data of SARS-CoV-2. Availability of relational dataset consisting of uniquely identified and linked patient and contacts was an advantage which enabled the adoption of network analytical methods.

Our adopted network analysis methods highlights the individual patient level variation in SARS-CoV-2 transmission. The out-degree centrality measures highlight that while the majority (88.72%) of the patients in the cohort had not transmitted infection, only a minuscule proportion of source patients (0.65%) have disproportionately transmitted the infection to 36.7% of target patients. In-degree, centrality measures shows that as much as 62.17% of diagnosed patients did not have any source of infection and less proportion of patients (1.72%) had more than one source of infection. Betweenness centrality measures highlights that not all infections were directly transmitted from few influential source patients (with higher out-degree) to many target patients, but only through influential bridging patients (with high betweenness) who transmitted the infection from them.

The centrality measures were thus able to identify and measure the differences among individual patients to infect others, getting infected and being able to transmit the infection as an intermediary to many others. The summary statistics of the out-degree and betweenness measures hints a heavy-tailed distribution with only a few actors in the network playing an influential role in being source and intermediaries of transmission. However, we have not attempted to prove the network distributional properties in this study since that would require a comprehensive social network survey data collected from SARS-CoV-2 patients and their contacts. The contact tracing data used by us do not have information on symptom onset, confirmation and related information which would be required for such analysis.

The network components analysis identified 19 key components which have contributed to almost two-thirds of the total transmission events between patient and contacts. Our findings show that the maximum transmission had happened within these nineteen components, which each proportionality represented only 3.22 to 0.5% of the total patients. The component analysis showed that source patients with high out-degree had passed their infection mostly through various intermediaries i.e. those with high betweenness. For example, in the identified giant component with 63 patients, (Fig. 1) it was identified that the first source patient (P52) had not transmitted infection to all 62 target patients by himself but through the considerable number of intermediaries. It was found that these individuals with high out-degrees and betweenness, (who could be called influencers) are predominantly found in these 19 components with have contributed to 68.7% of transmission. From this, we could hypothesize that influential patients as small connected components may contribute to much of the transmission events when compared to sole influencers or super spreaders [32,33,34].

The network measures and components could thus help the public health personnel to comprehend the key influential patients and patient groups (components) in their intervention settings. Basic network measures could be of importance for public health planners who would have to deal with contact tracing data of tens of thousands of patients on a daily basis and comprehend it. The quantitative measures and graphical tools of network method could be useful to analyze exhaustively and easily interpret the large scale contact tracing data which otherwise remain underutilized. We understand that contact tracing data on SARS-CoV-2 holds more valuable information and fine details which could be best explored by adopting complementary methods like social network methods, which has been successfully used in other infectious diseases contexts [11,12,13,14,15].

Our adopted social network analysis approach was found useful in capturing the heterogeneity of SARS-COV-2 transmission at the individual patient level by analyzing the contact tracing data from a network perspective. The method had helped identify the key individual patients and components which could help the public health implementers to focus their contact tracing activities. The network measures and graphical tools could complement the existing contact tracing indicators. Prospective adoption of network analysis could help explore large volumes of contact tracing data to detect heterogeneity and thus could aid implementing contact tracing activities in a better-informed manner.


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