This dissertation is motivated by an overarching research question: How do firms leverage external resources residing in their ego network (portfolio of alliances) and their clusters in order to innovate in a sustained manner? Research suggests that firms often struggle and falter in their innovation efforts. However, past research has paid little systematic attention on why firms struggle in their innovation efforts. Further, though network and clusters—the key sources of external resources—may overlap in several ways, the extant literature has not examined their joint effect on a firm's technological innovation. In this dissertation, using a longitudinal research design I examine how the characteristics of a firm's ego network and of its cluster independently and jointly impact its patent output in the U.S. semiconductor industry. The research provides a framework showing how networks and clusters may work in tandem in helping a firm overcome innovation barriers. The study demonstrates how firms can leverage network and cluster resources. The empirical evidence indicates that the efficacy of cluster resources increases in the presence of network ties within the cluster. It also shows that firms can mobilize resources of distant clusters using their network ties. The study further demonstrates that resource-rich firms leverage networks resources more effectively than the resource-deficient firms do while resource-deficient firms leverage cluster resources more effectively than the resource-rich firms do. The dissertation makes important theoretical and empirical contributions to alliance, network, cluster, and innovation literatures. The research findings also have important managerial implications. / Ph. D.

With the transition of the economy in China, migrants start holding a more and more important position in the labor market. Therefore, from this dissertation, we try to explore different topics related to the migrants in China. This dissertation consists of three essays on who chooses to migrate, how networks affect migrants' outcomes, and what is the intergenerational impact of parental migration on children's risk preferences. In the first chapter, we briefly introduce the motivation and contribution, and then we provide the methods and detailed findings in the following chapters. Chapter 2 examines the impact of the endogeneity of the decision to migrate on the wage differentials between migrants and non-migrants in China. We find that migrants are self-selected from the upper tail of the income distribution in their home location. Consistent with a theoretical model of migration choice, we show that the size of the selection effect on wage depends on the wage differences between the prefectures of origin and destination as well as migration cost. The selection effect also differs among workers with different education and in different cities. Chapter 3 studies how networks affect migrants' migration decisions, employment, and wage levels by using 2005 China's mini-census. Different from existing studies, this paper takes into account the existence of self-selection in the labor market. With the help of a theoretical model, we have a better understanding of the mechanism of networks as well as the different network effects on rural and urban migrants. We find out that networks affect both rural and urban migrants' migration decisions positively. In terms of employment, networks exert positive impacts on rural migrants but not on urban ones, which is due to the different quality drops between rural and urban migrants when the networks increase. Such employment effects also lead rural migrants to face a more severe negative wage impact than urban migrants. Chapter 4 investigates how parental migration affects left-behind underage children's risk preferences. By focusing on migrant parent groups, we are able to estimate whether the influence of nurture could also affect children's risk preference levels or not. The findings suggest that besides the intergenerational transmission of risk preferences between parents and children, parental migrations do have an influence on girls' risk preference levels. In addition, in terms of adult children's risk-related outcomes, we are able to find a positive parental migration impact on daughters' self-employment decisions. Findings help us have a better understanding of the relevant factors of risk preferences, and also confront the impact of the separation of parents and children. / Doctor of Philosophy / Nowadays, migration becomes increasingly common and migrants take a large proportion of the labor market. With the economic development and the closer connections among regions, people are more likely to study or work outside their home locations than before. Even though there is still a strict household registration system in China, we can find the migration supporting systems are becoming established, and facilitate easier migration for more and more people. Having a better understanding of migrants helps us make better policies as well as have the labor market and society develop better. This dissertation explores who choose to migrate (Chapter 2), how the social connections or networks affect migrants' employment and wages (Chapter 3), and whether there is any intergenerational impact on migrants' children in terms of risk preferences (Chapter 4) using methods from labor economics and economic theory. We conclude that the best works tend to migrate first in the labor market, and social connections have different impacts on rural and urban migrants in terms of employment and wages. Moreover, we notice that migrant parents affect the left-behind children's risk preferences by both influences of nature and nurture. In sum, we study different topics on migrants and have a deeper understanding of how migrants are affected in the labor market as well as how migrants affect their next generations.

Blockchain, the technology behind cryptocurrency, enables decentralized and distrustful parties to maintain a unique and consistent transaction history through consensus, without involving a central authority. The decentralization, transparency, and consensus-driven security promised by blockchain are unprecedented and can potentially enable a wide range of new applications that prevail in the decentralized zero-trust model. While blockchain represents a secure-by-design approach to building zero-trust applications, there still exist outstanding security bottlenecks that hinder the technology's wider adoption, represented by the following two challenges: (1) blockchain as a distributed networked system is multi-layered in nature which has complex security implications that are not yet fully understood or addressed; (2) when we use blockchain to construct new applications, especially those previously implemented in the centralized manner, there often lack effective paradigms to customize and augment blockchain's security offerings to realize domain-specific security goals. In this work, we provide answers to the above two challenges in two coordinated efforts. In the first effort, we target the fundamental security issues caused by blockchain's multi-layered nature and the consumption of external data. Existing analyses on blockchain consensus security overlooked an important cross-layer factor---the heterogeneity of the P2P network's connectivity. We first provide a comprehensive review on notable blockchain consensus protocols and their security properties. Then we focus one class of consensus protocol---the popular Nakamoto consensus---for which we propose a new analytical model from the networking perspective that quantifies the impact of heterogeneous network connectivity on key consensus security metrics, providing insights on the actual "51% attack" threshold (safety) and mining revenue distribution (fairness). The external data truthfulness challenge is another fundamental challenge concerning the decentralized applications running on top of blockchain. The validity of external data is key to the system's operational security but is out of the jurisdiction of blockchain consensus. We propose DecenTruth, a system that combines a data mining technique called truth discovery and Byzantine fault-tolerant consensus to enable decentralized nodes to collectively extract truthful information from data submitted by untrusted external sources. In the second effort, we harness the security offerings of blockchain's smart contract functionality along with external security tools to enable two domain-specific applications---data usage control and decentralized spectrum access system. First, we use blockchain to tackle a long-standing privacy challenge of data misuse. Individual data owners often lose control on how their data can be used once sharing the data with another party, epitomized by the Facebook-Cambridge Analytica data scandal. We propose PrivacyGuard, a security platform that combines blockchain smart contract and hardware trusted execution environment (TEE) to enable individual data owner's fine-grained control over the usage (e.g., which operation, who can use on what condition/price) of their private data. A core technical innovation of PrivacyGuard is the TEE-based execution and result commitment protocol, which extends blockchain's zero-trust security to the off-chain physical domain. Second, we employ blockchain to address the potential security and performance issues facing dynamic spectrum sharing in the 5G or next-G wireless networks. The current spectrum access system (SAS) designated by the FCC follows a centralized server-client service model which is vulnerable to single-point failures of SAS service providers and also lacks an efficient, automated inter-SAS synchronization mechanism. In response, we propose a blockchain-based decentralized SAS architecture dubbed BD-SAS to provide SAS service efficiently to spectrum users and enable automated inter-SAS synchronization, without assuming trust on individual SAS service providers. We hope this work can provide new insights into blockchain's fundamental security and applicability to new security domains. / Doctor of Philosophy / Blockchain, the technology behind cryptocurrency, enables decentralized and distrustful parties to maintain a unique and consistent transaction history through consensus, without involving a central authority. The decentralization, transparency, and consensus-driven security promised by blockchain are unprecedented and can potentially enable zero-trust applications in a wide range of domains. While blockchain's secure-by-design vision is truly inspiring, there still remain outstanding security challenges that hinder the technology's wider adoption. They originate from the blockchain system's complex multi-layer nature and the lack of effective paradigms to customize blockchain for domain-specific applications. In this work, we provide answers to the above two challenges in two coordinated efforts. In the first effort, we target the fundamental security issues caused by blockchain's multi-layered nature and the consumption of external data. We first provide a comprehensive review on existing notable consensus protocols and their security issues. Then we propose a new analytical model from a novel networking perspective that quantifies the impact of heterogeneous network connectivity on key consensus security metrics. Then we address the external data truthfulness challenge concerning the decentralized applications running on top of blockchain which consume the real-world data, by proposing DecenTruth, a system that combines data mining and consensus to allow decentralized blockchain nodes to collectively extract truthful information from untrusted external sources. In the second effort, we harness the security offerings of blockchain's smart contract functionality along with external security tools to enable two domain-specific applications. First, eyeing on our society's data misuse challenge where data owners often lose control on how their data can be used once sharing the data with another party, we propose PrivacyGuard, a security platform that combines blockchain smart contract and hardware security tools to give individual data owner's fine-grained control over the usage over their private data. Second, targeting the lack of a fault-tolerant spectrum access system in the domain of wireless networking, we propose a blockchain-based decentralized spectrum access system dubbed BD-SAS to provide spectrum management service efficiently to users without assuming trust on individual SAS service providers. We hope this work can provide new insights into blockchain's fundamental security and applicability to new security domains.

Abiotic stresses constitute a major category of stresses that negatively impact plant growth and development. It is important to understand how plants cope with environmental stresses and reprogram gene responses which in turn confers stress tolerance. Recent advances of genomic technologies have led to the generation of much genomic data for the model plant, Arabidopsis. To understand gene responses activated by specific external stress signals, these large-scale data sets need to be analyzed to generate new insight of gene functions in stress responses. This poses new computational challenges of mining gene associations and reconstructing regulatory interactions from large-scale data sets. In this dissertation, several computational tools were developed to address the challenges. In Chapter 2, ConSReg was developed to infer condition-specific regulatory interactions and prioritize transcription factors (TFs) that are likely to play condition specific regulatory roles. Comprehensive investigation was performed to optimize the performance of ConSReg and a systematic recovery of nitrogen response TFs was performed to evaluate ConSReg. In Chapter 3, CoReg was developed to infer co-regulation between genes, using only regulatory networks as input. CoReg was compared to other computational methods and the results showed that CoReg outperformed other methods. CoReg was further applied to identified modules in regulatory network generated from DAP-seq (DNA affinity purification sequencing). Using a large expression dataset generated under many abiotic stress treatments, many regulatory modules with common regulatory edges were found to be highly co-expressed, suggesting that target modules are structurally stable modules under abiotic stress conditions. In Chapter 4, exploratory analysis was performed to classify cell types for Arabidopsis root single cell RNA-seq data. This is a first step towards construction of a cell-type-specific regulatory network for Arabidopsis root cells, which is important for improving current understanding of stress response. / Doctor of Philosophy / Abiotic stresses constitute a major category of stresses that negatively impact plant growth and development. It is important to understand how plants cope with environmental stresses and reprogram gene responses which in turn confers stress tolerance to plants. Genomics technology has been used in past decade to generate gene expression data under different abiotic stresses for the model plant, Arabidopsis. Recent new genomic technologies, such as DAP-seq, have generated large scale regulatory maps that provide information regarding which gene has the potential to regulate other genes in the genome. However, this technology does not provide context specific interactions. It is unknown which transcription factor can regulate which gene under a specific abiotic stress condition. To address this challenge, several computational tools were developed to identify regulatory interactions and co-regulating genes for stress response. In addition, using single cell RNA-seq data generated from the model plant organism Arabidopsis, preliminary analysis was performed to build model that classifies Arabidopsis root cell types. This analysis is the first step towards the ultimate goal of constructing cell-typespecific regulatory network for Arabidopsis, which is important for improving current understanding of stress response in plants.

Given a population contact network and electronic medical records of patients, how to distribute vaccines to individuals to effectively control a flu epidemic? Similarly, given the Twitter following network and tweets, how to choose the best communities/groups to stop rumors from spreading? How to find the best accounts that bridge celebrities and ordinary users? These questions are related to diffusion (aka propagation) phenomena. Diffusion can be treated as a behavior of spreading contagions (like viruses, ideas, memes, etc.) on some underlying network. It is omnipresent in areas such as social media, public health, and cyber security. Examples include diseases like flu spreading on person-to-person contact networks, memes disseminating by online adoption over online friendship networks, and malware propagating among computer networks. When a contagion spreads, network structure (like nodes/edges/groups, etc.) plays a major role in determining the outcome. For instance, a rumor, if propagated by celebrities, can go viral. Similarly, an epidemic can die out quickly, if vulnerable demographic groups are successfully targeted for vaccination. Hence in this thesis, we aim to optimize and understand network structure better in light of diffusion. We optimize graph topologies by removing nodes/edges for controlling rumors/viruses from spreading, and gain a deeper understanding of a network in terms of diffusion by exploring how nodes group together for similar roles of dissemination. We develop several novel graph mining algorithms, with different levels of granularity (node/edge level to group/community level), from model-driven and data-driven perspectives, focusing on topics like immunization on networks, graph summarization, and community detection. In contrast to previous work, we are the first to systematically develops more realistic, implementable and data-based graph algorithms to control contagions. In addition, our thesis is also the first work to use diffusion to effectively summarize graphs and understand communities/groups of networks in a general way. 1. Model-driven. Diffusion processes are usually described using mathematical models, e.g., the Independent Cascade (IC) model in social media, and the Susceptible-Infectious-Recovered (SIR) model in epidemiology. Given such models, we propose to optimize network structure for controlling propagation (the immunization problem) in several practical and implementable settings, taking into account the presence of infections, the uncertain nature of the data and group structure of the population. We develop efficient algorithms for different interventions, such as vaccination (node removal) and quarantining (edge removal). In addition, we study the graph coarsening problem for both static and temporal networks to obtain a better understanding of relations among nodes when a contagion is propagating. We seek to get a much smaller representation of a large network, while preserving its diffusive properties. 2. Data-driven. Model-driven approaches can provide ideal results if underlying diffusion models are given. However, in many situations, diffusion processes are very complicated, and it is challenging or even impossible to pick the most suited model to describe them. In addition, rapid technological development has provided an abundance of data such as tweets and electronic medical records. Hence, in the second part of the thesis, we explore data-driven approaches for diffusion in networks, which can directly work on propagation data by relaxing modeling assumptions of diffusion. To be specific, we first develop data-driven immunization strategies to stop rumors or allocate vaccines by optimizing network topologies, using large-scale national-level diagnostic patient data with billions of flu records. Second, we propose a novel community detection problem to discover "bridge" and "celebrity" communities from social media data, and design case studies to understand roles of nodes/communities using diffusion. Our work has many applications in multiple areas such as epidemiology, sociology and computer science. For example, our work on efficient immunization algorithms, such as data-driven immunization, can help CDC better allocate vaccines to control flu epidemics in major cities. Similarly, in social media, our work on understanding network structure using diffusion can lead to better community discovery, such as finding media accounts that can boost tweet promotions in Twitter. / Ph. D.

Due to an exponentially larger address space than Internet Protocol version 4 (IPv4), the Internet Protocol version 6 (IPv6) uses new methods to assign network addresses to Internet nodes. StateLess Address Auto Configuration (SLAAC) creates an address using a static value derived from the Media Access Control (MAC) address of a network interface as host portion, or interface identifier (IID). The Dynamic Host Configuration Protocol version 6 (DHCPv6) uses a client-server model to manage network addresses, providing stateful address configuration. While DHCPv6 can be configured to assign randomly distributed addresses, the DHCP Unique Identifier (DUID) was designed to remain static for clients as they move between different DHCPv6 subnets and networks. Both the IID and DUID are static values which are publicly exposed, creating a privacy and security threat for users and nodes. The static IID and DUID allow attackers to violate unsuspecting IPv6 users' privacy and security with ease. These static identifiers make geographic tracking and network traffic correlation over multiple sessions simple. Also, different classes of computer and network attacks, such as system-specific attacks and Denial-of-Service (DoS) attacks, are easier to successfully employ due to these identifiers. This research identifies and tests the validity of the privacy and security threat of static IIDs and DUIDs. Solutions which mitigate or eliminate the threat posed by static identifiers in IPv6 are identified. / Master of Science

Cells generally manage to maintain stable phenotypes in the face of widely varying environmental conditions. This fact is particularly surprising since the key step of gene expression is fundamentally a stochastic process. Many hypotheses have been suggested to explain this robustness. First, the special topology of gene regulatory networks (GRNs) seems to be an important factor as they possess feedforward loops and certain other topological features much more frequently than expected. Second, genes often regulate each other in a canalizing fashion: there exists a dominance order amidst the regulators of a gene, which in silico leads to very robust phenotypes. Lastly, an entirely novel gene regulatory mechanism, discovered and studied during the last two decades, which is believed to play an important role in cancer, is shedding some light on how canalization may in fact take place as part of a cell’s gene regulatory program. Short segments of single-stranded RNA, so-called microRNAs, which are embedded in several different types of feedforward loops, help smooth out noise and generate canalizing effects in gene regulation by overriding the effect of certain genes on others. Boolean networks and their multi-state extensions have been successfully used to model GRNs for many years. In this dissertation, GRNs are represented in the time- and statediscrete framework of Stochastic Discrete Dynamical Systems (SDDS), which captures the cell-inherent stochasticity. Each gene has finitely many different concentration levels and its concentration at the next time step is determined by a gene-specific update rule that depends on the current concentration of the gene’s regulators. The update rules in published gene regulatory networks are often nested canalizing functions. In Chapter 2, this class of functions is introduced, generalized and analyzed with respect to its potential to confer robustness. Chapter 3 describes a simulation study, which supports the hypothesis that microRNA-mediated feedforward loops have a stabilizing effect on GRNs. Chapter 4 focuses on the cellular DNA mismatch repair machinery. A first regulatory network for this machinery is introduced, partly validated and analyzed with regard to the role of microRNAs and certain genes in conferring robustness to this particular network. Due to steady exposure to mutations, GRNs have evolved over time into their current form. In Chapter 5, a new framework for modeling the evolution of GRNs is developed and then used to identify topological features that seem to stabilize GRNs on an evolutionary time-scale. Chapter 6 addresses a completely separate project in Bioinformatics. A novel functional enrichment method is developed and compared to various popular methods. Funding for this work was provided by NSF grant CMMI-0908201 and NSF grant 1062878. / Ph. D.

Communications over social media, telephone, email, text etc have emerged as an integral part of modern society and they are popularly used for the expression of anger, anxiety, fear, agitation and opinion by the people. People's social interaction tend to increase dramatically during periods of epidemics, protest and calamities. Therefore, above mentioned communication channels plays an important role in the spread of infectious phenomenon, like rumors, fads and effects. These infectious phenomena alters people's behavior during disease epidemic [1][2]. Social contact networks and epidemics co-evolve [1][2]. The spread of a disease influences people's behavior which in turn changes their social contact network, thereby altering the disease spread itself. As a result, there is a need for modeling the spread of these infectious phenomena that lead to changes in behavior. Their propagation among population primarily depends on the social contact network. The nature of social contagion spread is very similar to the spread of any infectious disease as they are contagious in nature. To spread contagious disease requires direct exposure to an infectious agent, whereas social contagions can be spread using various communications media like social networking forums, phones, emails and tweets. EpiSimdemics is an individual-based modeling environment. It uses a people-location bipartite graph as the underlying network [3]. In its current form, EpiSimdemics requires two people to interact at a location to model simulations. Thus, it cannot simulate the spread of social contagions that do not necessarily require the meeting of two agents at a location. We enhance EpiSimdemics by incorporating Person-Person network, which can model communications between people that are not contact based such as communications over email, phone, text and tweet. This Person-Person network is used to model effects (social contagion) which induce behavioral changes in population and thus impacting the disease spread. The disease spread is modeled on Person-Location network. This leads to the scenario of two interacting networks: Person-Person network modeling social contagion and Person-Location modeling disease. Theoretically, there can be multiple such networks modeling various interacting phenomena. We demonstrate the usefulness of this network by modeling and simulating two interacting PTTSs (probabilistic timed transition systems). To model disease epidemics, we have defined Disease Model and to model effects (social contagion), we have defined Fear Model. We show how these models influence each other by performing simulations on EpiSimdemics with interacting Disease and Fear Model. Therefore a model that does not include the affect adaptations on disease epidemics and vice-versa, fails to reflect the actual behavior of a society during disease epidemic spread. The addition of Person-Person network to EpiSimdemics will allow for a better understanding of the affect adaptions, which can include behavior changes in society during an epidemic outbreak. This would lead to effective interventions and help to better understand the dynamics of disease epidemic. / Master of Science

Mobile device security is becoming increasingly important as the number of devices that are used continues to grow and has surpassed one billion active devices globally. In this thesis, we will investigate the security of Android ad supported apps, security vulnerabilities that have been identified in the way those ads are delivered to the device and improvements that can be made to protect the privacy of the end user. To do this, we will discuss the Android architecture and the ecosystems of apps and ads on those devices. To better understand the threats to mobile devices, a threat analysis will be conducted, investigating the different attack vectors that devices are susceptible to. This will also include a survey of existing work that has been conducted within the realm of Android security and web based exploits. The specific attacks that are detailed in this research are addJavascriptInterface attacks against a WebView used to display an ad and information leakage from the ad URL request. These attack vectors are discussed in detail with applicability and feasibility studies conducted. The results of these attacks will be analyzed with a discussion of the methodology used to obtain them. In order to combat such attacks, there will also be discussion of potential solutions to mitigate the threats of attack from a variety of angles, to include steps that users can take to protect themselves as well as changes that should be made to the Android operating system itself. / Master of Science

Yes / In an academic field, where does brand new idea come from? To understand how noble ideas emerge, this study elucidates how network brokers and high status actors contribute to the creation of knowledge institutions, by paying a specific attention to the interplay between institutional structure and an individual agency in academia. Although numerous scholars have been attempted to relieve the tensions around the agency versus structure debate, accurate explanations of interactive aspects between them are not well documented. To fill this void, this study suggests a conceptual model to explain the complementary and synergetic effects of network structure and agency on the knowledge innovation. In doing so, this study provide an answer to the question of why some actors often fail to obtain significant advantages from a privileged network position while others succeed.