The purpose of this study is to explore the mechanics of guanxi in an organizational setting, focusing on the use of interpersonal relationships within Chinese firms to discover how firms initiate, build and use guanxi networks. Two richly detailed case studies document changes that take place over time in two distinct networks with respect to key actors and their contacts. This research also investigates patterns of social structure that emerge over time in these two distinct cases looking at brokerage relationships, network density, and dyadic redundancy in three waves at six month intervals. The cases are dissimilar in all aspects except absolute size demonstrating the universal use of guanxi across time, geographic location, specific industries, and firm experience. Dynamic network visualization is used to highlight the sequence and rate of activity in each network to identify salient changes. The findings show that firms seek to improve their organizational guanxi by improving existing employees' guanxi quality within the firm and by recruiting new actors from outside the firm. Additionally, firms use organizational guanxi to expand their networks by forming cooperative partnerships with complementary organizations that enhance the attributes or potential of both organizations. And finally, firms initially exploit brokerage in organizational guanxi, then attempt to stabilize the network by fostering new ties to exclusive contacts.

This study examines a largely unexplored aspect of Sutherland’s (1974) model of differential association: the interplay of general and crime specific definitions favorable towards crime. Do individuals learn the specific techniques of a type of crime through interactions or do social interactions produce a general disposition towards all types of criminal behavior? Little prior research has been done on the influence of these definitions. Instead studies focus on only one or another, which leaves the details of general/specific definitions unexplored. With the aid of a mixed methodology of statistical and network analysis, this study explores general/specific definitions simultaneously by focusing on relationships between egos and alters. If alters commit similar crimes, it is likely that crime specific definitions are being learned; if crimes are dissimilar then general definitions are more likely. Using police data on a known criminal network located in an urban capital, I test the relationship between the criminal behaviors of egos and alters. The study also compares the centrality of the node to the commonality of crime they commit. This provides an understanding of how key nodes in the network affect the dissemination of criminal definitions. Overall, while variations exist for criminal types, the study finds that crime specific definitions dominate the network and, therefore, have greater influence over respondents’ criminal behavior. Conversely, I found no clear pattern which indicates that high centrality nodes commit more common crimes. This may indicate that high centrality nodes are responsible for disseminating general definitions of crime while most nodes communicate crime specific definition.

The astonishing progress of molecular biology, engineering and computer science has resulted in mature technologies capable of examining multiple cellular components at a genome-wide scale. Protein-protein interactions are one example of such growing data. These data are often organised as networks with proteins as nodes and interactions as edges. Albeit still incomplete, there is now a substantial amount of data available and there is a need for biologically meaningful methods to analyse and interpret these interactions. In this thesis we focus on how to compare protein interaction networks (PINs) and on the rela- tionship between network architecture and the biological characteristics of proteins. The underlying theme throughout the dissertation is the use of small subgraphs – small interaction patterns between 2-5 proteins. We start by examining two popular scores that are used to compare PINs and network models. When comparing networks of the same model type we find that the typical scores are highly unstable and depend on the number of nodes and edges in the networks. This is unsatisfactory and we propose a method based on non-parametric statistics to make more meaningful comparisons. We also employ principal component analysis to judge model fit according to subgraph counts. From these analyses we show that no current model fits to the PINs; this may well reflect our lack of knowledge on the evolution of protein interactions. Thus, we use explanatory variables such as protein age and protein structural class to find patterns in the interactions and subgraphs we observe. We discover that the yeast PIN is highly heterogeneous and therefore no single model is likely to fit the network. Instead, we focus on ego-networks containing an initial protein plus its interacting partners and their interaction partners. In the final chapter we propose a new, alignment-free method for network comparison based on such ego-networks. The method compares subgraph counts in neighbourhoods within PINs in an averaging, many-to-many fashion. It clusters networks of the same model type and is able to successfully reconstruct species phylogenies solely based on PIN data providing exciting new directions for future research.