One policy reaction of the Federal Reserve to the 2008 financial crisis was the extension of currency swap lines to various foreign central banks; this constituted the global transfer of billions of US dollars of wealth and exhibited the role of the US as a global lender of last resorts. Some have attempted to explain the supply of these lines as a function of risk mitigation for domestic US banks with foreign holdings, but no one has yet investigated the social dynamics of this phenomenon. In recognizing that the global demand for emergency liquidity was greater than the Federal Reserve’s supply, this paper investigates how the similarity of foreign central banks affected the selection of which banks would receive liquidity extensions. I calculate similarity scores to the US Federal Reserve for foreign banks which applied for liquidity extensions during the crisis. These scores measure the textual similarity of foreign central bankers’ speeches to those of the Fed, the institutional design similarity to that of the Fed, and the similarity of foreign central banks’ governors’ educational and professional backgrounds to those of the 2008 Federal Open Markets Commission members. I find that the similarity of foreign central banks to the US with regard to these three criteria offers a significantly stronger and statistically more robust answer to the question of what drove this decision process, and offer implications for international regulatory mechanisms to ameliorate this tendency toward social homophily.

The astronomical increase in the number of wireless devices such as smart phones in 21th century has revolutionized the way people communicate with one another and share information. The new wireless technologies have also enabled researchers to collect real data about how people move and meet one another in different social settings. Understanding human mobility has many applications in different areas such as traffic planning in cities and public health studies of epidemic diseases. In this thesis, we study the fundamental properties of human contact graphs in order to characterize how people meet one another in different social environments. Understanding human contact patterns in return allows us to propose a cost-effective routing algorithm for spreading information in Delay Tolerant Networks. Furthermore, we propose several contact predictors to predict the unobserved parts of contact graphs when only partial observations are available. Our results show that we are able to infer hidden contacts of real contact traces by exploiting the underlying properties of contact graphs. In the last few years, we have also witnessed an explosion in the number of people who use social media to share information with their friends. In the last part of this thesis, we study the running times of several information spreading algorithms in social networks in order to find the fastest strategy. Fast information spreading has an obvious application in advertising a product to a large number of people in a short amount of time. We prove that a fast information spreading algorithm should efficiently identify communication bottlenecks in order to speed up the running time. Finally, we show that sparsifying large social graphs by exploiting the edge-betweenness centrality measure can also speed up the information spreading rate. / Graduate