Abstract
This thesis is divided into two distinct chapters. In the first chapter, we apply network representation learning to the field of materials science in order to predict aluminum grain boundaries' properties and locate the most influential atoms and subgraphs within each grain boundary. We create fixed-length representations of the aluminum grain boundaries that successfully capture grain boundary structure and allow us to accurately predict grain boundary energy. We do this through two distinct methods. The first method we use is a graph convolutional neural network, a semi-supervised deep learning algorithm, and the second method is graph2vec, an unsupervised representation learning algorithm. The second chapter presents our dynamic global value chain network, the combination of the dynamic global supply chain network and the dynamic global strategic alliance network. Our global value chain network provides a level of scope and accessibility not found in any other global value chain network, commercial or academic. Through applications of network theory, we discover business applications that would increase the robustness and resilience of the global value chain. We accomplish this through an analysis of the static, dynamic, and community structure of our global value chain network.
Degree
MS
College and Department
Physical and Mathematical Sciences; Mathematics
Rights
https://lib.byu.edu/about/copyright/
BYU ScholarsArchive Citation
Haneberg, Mats C., "Network Representation Theory in Materials Science and Global Value Chain Analysis" (2023). Theses and Dissertations. 9869.
https://scholarsarchive.byu.edu/etd/9869
Date Submitted
2023-04-07
Document Type
Thesis
Handle
http://hdl.lib.byu.edu/1877/etd12707
Keywords
network representation learning, network theory, graph convolutional neural network, network community structure, supply chain, strategic alliance
Language
english