Advances to network analysis theories and methods with applications in social, organizational, and crisis settings

This dissertation proposes several solutions to the advancement of network analysis theories and methods with specific applications in the domains of social, organizational, and crisis scenarios. The field of network analysis has attracted interest from scholars coming from a wide range of disciplines as it provides valuable theoretical and methodological toolkits to investigate complex systems of social relations. Furthermore, network theories and methods can examine dynamics present at multiple levels of analysis, from individual- to global-levels. As a result, network analysis has been applied to various contexts of social science research such as social interactions, organizational communication, and crisis response collaboration. In this thesis, I present substantive insights into the application of several network analysis theories and applications to the (1) social, (2) organizational, and (3) crisis response settings. For the context of social interactions, I expand structural balance evaluation to signed and directed networks, and apply this approach to examine 12 social networks. For the context of organizational communication, I demonstrate the application of multilevel modeling for egocentric networks to examine factors associated with the formation of interdisciplinary ties in a scientific organization. In addition, I leverage an extended version of structural balance evaluation for signed and directed networks to examine the sources of tension present in three organizational networks. Third, I provide a case study of response dynamics during the 2010 Haiti earthquake by examining collaboration networks prescribed by national guidelines for response, and interaction networks of the actual collaborations that took place during the earthquake response. Altogether, this work contributes to the growing literature on the theories and applications of network analysis to real-world social networks. In particular, the study designs and findings developed in this thesis can provide a framework for network-based studies from many domains of interest, that includes components of network theories and methods that can help explain the social mechanisms involved in tie formation

From Negative Aspects and Black Aspects of Vietnam Education to Lessons for Social Sciences Students

In our research, we level strong criticism at the State Professor Council in Vietnam, using the case of Tran Van Tho and other members as a representative illustration of their efforts to impede or restrict the growth of the nation's scientific community. We also do not place enough importance on the publication of bogus news online by various Vietnam publications (Thanhnien.vn and Tuoitre.vn), despite the fact that it may lead to confusion, as well as problems and concern in the community. In addition, we oppose the excessive tuition fees and other expenses that have been imposed by Banking University HCM city Vietnam and other institutions in HCM city in recent years, which have caused difficulties for families, parents, students, and society as a whole. In addition, we use these problematic aspects of the education system in Vietnam as a case study to instruct students majoring in social sciences. After that, the authors apply a strategy based on the laws of Malaysia in order to solve the problem of fake news published in the newspapers tuoi tre and thanh nien in Vietnam throughout the period 2015-2022. After the case discussion that was offered earlier, the authors assess the opinions of President Ho Chi Minh on publishing activities for the purpose of better teaching pupils. This is not the least of the authors' contributions

Information-Theoretically Secure Communication Under Channel Uncertainty

Secure communication under channel uncertainty is an important and challenging problem in physical-layer security and cryptography. In this dissertation, we take a fundamental information-theoretic view at three concrete settings and use them to shed insight into efficient secure communication techniques for different scenarios under channel uncertainty. First, a multi-input multi-output (MIMO) Gaussian broadcast channel with two receivers and two messages: a common message intended for both receivers (i.e., channel uncertainty for decoding the common message at the receivers) and a confidential message intended for one of the receivers but needing to be kept asymptotically perfectly secret from the other is considered. A matrix characterization of the secrecy capacity region is established via a channel-enhancement argument and an extremal entropy inequality previously established for characterizing the capacity region of a degraded compound MIMO Gaussian broadcast channel. Second, a multilevel security wiretap channel where there is one possible realization for the legitimate receiver channel but multiple possible realizations for the eavesdropper channel (i.e., channel uncertainty at the eavesdropper) is considered. A coding scheme is designed such that the number of secure bits delivered to the legitimate receiver depends on the actual realization of the eavesdropper channel. More specifically, when the eavesdropper channel realization is weak, all bits delivered to the legitimate receiver need to be secure. In addition, when the eavesdropper channel realization is strong, a prescribed part of the bits needs to remain secure. We call such codes security embedding codes, referring to the fact that high-security bits are now embedded into the low-security ones. We show that the key to achieving efficient security embedding is to jointly encode the low-security and high-security bits. In particular, the low-security bits can be used as (part of) the transmitter randomness to protect the high-security ones. Finally, motivated by the recent interest in building secure, robust and efficient distributed information storage systems, the problem of secure symmetrical multilevel diversity coding (S-SMDC) is considered. This is a setting where there are channel uncertainties at both the legitimate receiver and the eavesdropper. The problem of encoding individual sources is first studied. A precise characterization of the entire admissible rate region is established via a connection to the problem of secure coding over a three-layer wiretap network and utilizing some basic polyhedral structure of the admissible rate region. Building on this result, it is then shown that the simple coding strategy of separately encoding individual sources at the encoders can achieve the minimum sum rate for the general S-SMDC problem

Middle of the (by)line: Examining hyperauthorship networks in the Human Genome Project

The era of big science promises rapid growth of scientific innovations and complex problem-solving, bringing forth the practice of doing science in large-scale collaborative effort rather than single author, solitary work. In disciplines such as genomics and high-energy physics, it is not uncommon that hyperauthorship phenomenon with the amount of authors soaring high from hundredth to thousandth. The purpose of this research is to explore the collaboration dynamics and the partial alphabetical author byline in one of the primary research article within the Human Genome Project (HGP). Using co-authorship network analysis, we find that middle authors play crucial roles in facilitating collaboration among previously unconnected authors as well as contributing to genetic sequencing efforts. Future work entails network analysis of all published works by HGP to comprehensively capture collaboration dynamics among multiple research centers.Goggin fundSmith fundOpe

Middle of the (by)line: Examining hyperauthorship networks in the Human Genome Project

The era of big science promises rapid growth of scientific innovations and complex problem-solving, bringing forth the practice of doing science in large-scale collaborative effort rather than single author, solitary work. In disciplines such as genomics and high-energy physics, it is not uncommon that hyperauthorship phenomenon with the amount of authors soaring high from hundredth to thousandth. The purpose of this research is to explore the collaboration dynamics and the partial alphabetical author byline in one of the primary research article within the Human Genome Project (HGP). Using co-authorship network analysis, we find that middle authors play crucial roles in facilitating collaboration among previously unconnected authors as well as contributing to genetic sequencing efforts. Future work entails network analysis of all published works by HGP to comprehensively capture collaboration dynamics among multiple research centers.Goggin fundSmith fundOpe

Energy Efficient Resource Allocation Optimization in Fog Radio Access Networks with Outdated Channel Knowledge

Fog Radio Access Networks (F-RAN) are gaining worldwide interests for enabling mobile edge computing for Beyond 5G. However, to realize the future real-time and delay-sensitive applications, F-RAN tailored radio resource allocation and interference management become necessary. This work investigates user association and beamforming issues for providing energy efficient F-RANs. We formulate the energy efficiency maximization problem, where the F-RAN specific constraint to guarantee local edge processing is explicitly considered. To solve this intricate problem, we design an algorithm based on the Augmented Lagrangian (AL) method. Then, to alleviate the computational complexity, a heuristic low-complexity strategy is developed, where the tasks are split in two parts: one solving for user association and Fog Access Points (F-AP) activation in a centralized manner at the cloud, based on global but outdated user Channel State Information (CSI) to account for fronthaul delays, and the second solving for beamforming in a distributed manner at each active F-AP based on perfect but local CSIs. Simulation results show that the proposed heuristic method achieves an appreciable performance level as compared to the AL-based method, while largely outperforming the energy efficiency of the baseline F-RAN scheme and limiting the sum-rate degradation compared to the optimized sum-rate maximization algorithm

An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events

In times of crisis, identifying the essential needs is a crucial step to providing appropriate resources and services to affected entities. Social media platforms such as Twitter contain vast amount of information about the general public's needs. However, the sparsity of the information as well as the amount of noisy content present a challenge to practitioners to effectively identify shared information on these platforms. In this study, we propose two novel methods for two distinct but related needs detection tasks: the identification of 1) a list of resources needed ranked by priority, and 2) sentences that specify who-needs-what resources. We evaluated our methods on a set of tweets about the COVID-19 crisis. For task 1 (detecting top needs), we compared our results against two given lists of resources and achieved 64% precision. For task 2 (detecting who-needs-what), we compared our results on a set of 1,000 annotated tweets and achieved a 68% F1-score