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Performing social network analysis (SNA) requires a set of powerful techniques to analyze structural information contained in interactions between social entities. Many SNA technologies and methodologies have been developed and have successfully provided significant insights for small-scale interactions. However, these techniques are not suitable for analyzing large social networks, which are very popular and important in various fields and have special structural properties that cannot be obtained from small networks or their analyses. There are a number of issues that need to be further studied in the design of current SNA techniques. A number of key issues can be embodied in three fundamental and critical challenges: long processing time, large computational resource requirements, and network dynamism. In order to address these challenges, we discuss an anytime-anywhere methodology based on a parallel/distributed computational framework to effectively and efficiently analyze large and dynamic social networks. In our methodology, large social networks are decomposed into intra-related smaller parts. A coarse-level of network analysis is built based on comprehensively analyzing each part. The partial analysis results are incrementally refined over time. Also, during the analyses process, network dynamic changes are effectively and efficiently adapted based on the obtained results. In order to evaluate and validate our methodology, we implement our methodology for a set of SNA metrics which are significant for SNA applications and cover a wide range of difficulties. Through rigorous theoretical and experimental analyses, we demonstrate that our anytime-anywhere methodology is / Ph. D.

This dissertation consists of three essays in Health Economics relating to the recent challenges in the U.S. The first essay studies the impact of retirement on subsequent health and investigates the mediation effect of social network in the relationship between retirement and health. Findings reveal that retirement adversely impacts physical and mental health outcomes and a considerable portion of these effects are explained by social network changes post-retirement. In particular, shrinkage in the size of social network post-retirement deteriorates physical health and increases depression in retirees. In the second essay, we assess the differential effect of social distancing on the daily growth rate of COVID-19 infections in the US counties by considering the spatial pattern of COVID-19 spread. We also conduct a comparative analysis of the effect on urban versus rural counties, as well as low versus high socially vulnerable counties. Our analysis illustrates that a high level of social distancing compliance is needed in urban counties and in socially vulnerable areas to achieve the largest impact at curve flattening, whereas moderate-compliance is enough in reaching the peak marginal impact in rural regions and counties with low social vulnerability. In the third essay, by combining multiple data sources, we investigate how racial disparities in access to healthcare contribute to the disparity in COVID-19 infections and mortality in black versus white sub-groups. The multilevel analysis demonstrates that a higher probability of having health insurance significantly reduces disparity in COVID-19 mortality in black sub-group while it has no impact on the disparity in whites. / Doctor of Philosophy / This dissertation uses various quantitative methods to investigate policy-relevant questions regarding the recent challenges in the U.S. economy. In the first chapter, we explore how the physical and mental health of individuals changes by retirement. The results show that retirement decreases physical health while increases depression and anxiety. We also analyze how social network changes after retirement might cause changes in the health of retirees. We find that retirees may experience worse physical and mental health than non-retirees due to losing some of their relationships after retirement. In particular, the loss of contacts increases depression and deteriorates general health. In the second chapter, we investigate how compliance with social distancing within a typical county and its neighbor counties can reduce the spread of COVID-19. We examine this question for urban versus rural counties in the US and socially vulnerable versus socially not vulnerable counties. We find a high compliance level of social distancing is needed in urban counties and in socially vulnerable areas to reach the highest impact at slowing down the COVID-19 virus spread. In the third chapter, we examine whether healthcare access inequalities (e.g., having health insurance) increase the risk of COVID-19 infections and mortality for black communities. Our results show that having health insurance decreases COVID-19 mortality in communities of color but not whites.

I analyze the relationship between insider trading outcomes and insiders' information environment within a network. While most existing studies rely on one dimension of commonality (e.g., personal ties, professional ties, geographic proximity) to construct the social network, I document the formation of the institutional investor groups (cliques) that exogenously connect firm-level insiders within the social network. Using difference-in-differences designs examining changes in clique size, I provide empirical evidence on the information dissemination channels within a network in which its members are quasi-randomly selected. Insider transactions in larger cliques exhibit lower abnormal trading profits, higher level of trading frequency, and larger amount of trade size, suggesting information dissemination is increasing in clique size. Then, I provide empirical evidence that the association between the value of information and the information dissemination rate is monotonic, consistent with prior theoretical studies. / Doctor of Philosophy / People communicate and are influenced by other people when they reside in a social network. I analyze how corporate insiders' trading outcomes are influenced by their information environment within a network. Most current research rely on one specific type of connection (e.g., personal relationships, professional relationships, geographic proximity) to build the social network, I provide evidence that firm-level insiders are involuntarily connected by the institutional investor social network (cliques). Using archival study approach, I document that insider transactions in larger cliques exhibit lower abnormal trading profits, higher level of trading frequency, and larger amount of trade size, suggesting information dissemination is increasing in clique size. Then, I provide empirical evidence that the association between the value of information and the information dissemination rate is linear, consistent with prior theoretical studies.

学位プログラム名: 京都大学大学院思修館 / 京都大学 / 新制・課程博士 / 博士(総合学術) / 甲第25458号 / 総総博第34号 / 新制||総総||6(附属図書館) / 京都大学大学院総合生存学館総合生存学専攻 / (主査)准教授 趙 亮, 教授 山敷 庸亮, 准教授 SCHMOECKERJan-Dirk / 学位規則第4条第1項該当 / Doctor of Philosophy / Kyoto University / DFAM