ETDs

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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Poincare Embeddings for Visualizing Eigenvector Centrality

Description
Hyperbolic geometry, which is a geometry which concerns itself with hyperbolic space, has caught the eye of certain circles in the machine learning community as of late. Lauded for its ability to encapsulate strong clustering as well as latent hierarchies in complex and social networks, hyperbolic geometry has proven itself…

Hyperbolic geometry, which is a geometry which concerns itself with hyperbolic space, has caught the eye of certain circles in the machine learning community as of late. Lauded for its ability to encapsulate strong clustering as well as latent hierarchies in complex and social networks, hyperbolic geometry has proven itself to be an enduring presence in the network science community throughout the 2010s, with no signs of fading into obscurity anytime soon. Hyperbolic embeddings, which map a given graph to hyperbolic space, have particularly proven to be a powerful and dynamic tool for studying complex networks. Hyperbolic embeddings are exploited in this thesis to illustrate centrality in a graph. In network science, centrality quantifies the influence of individual nodes in a graph. Eigenvector centrality is one type of such measure, and assigns an influence weight to each node in a graph by solving for an eigenvector equation. A procedure is defined to embed a given network in a model of hyperbolic space, known as the Poincare disk, according to the influence weights computed by three eigenvector centrality measures: the PageRank algorithm, the Hyperlink-Induced Topic Search (HITS) algorithm, and the Pinski-Narin algorithm. The resulting embeddings are shown to accurately and meaningfully reflect each node's influence and proximity to influential nodes.
ContributorsChang, Alena (Author) / Xue, Guoliang (Thesis advisor) / Yang, Dejun (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020