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MULTIFACETED EMBEDDING LEARNING FOR NETWORKED DATA AND SYSTEMS

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Date Issued:
2020
Abstract/Description:
Network embedding or representation learning is important for analyzing many real-world applications and systems, i.e., social networks, citation networks and communication networks. It targets at learning low-dimensional vector representations of nodes with preserved graph structure (e.g., link relations) and content (e.g., texts) information. The derived node representations can be directly applied in many downstream applications, including node classification, clustering and visualization. In addition to the complex network structures, nodes may have rich non structure information such as labels and contents. Therefore, structure, label and content constitute different aspects of the entire network system that reflect node similarities from multiple complementary facets. This thesis focuses on multifaceted network embedding learning, which aims to efficiently incorporate distinct aspects of information such as node labels and node contents for cooperative low-dimensional representation learning together with node topology.
Title: MULTIFACETED EMBEDDING LEARNING FOR NETWORKED DATA AND SYSTEMS.
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Name(s): Shi, Min , author
Tang, Yufei, Thesis advisor
Florida Atlantic University, Degree grantor
Department of Computer and Electrical Engineering and Computer Science
College of Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2020
Date Issued: 2020
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: online resource
Extent: 146 p.
Language(s): English
Abstract/Description: Network embedding or representation learning is important for analyzing many real-world applications and systems, i.e., social networks, citation networks and communication networks. It targets at learning low-dimensional vector representations of nodes with preserved graph structure (e.g., link relations) and content (e.g., texts) information. The derived node representations can be directly applied in many downstream applications, including node classification, clustering and visualization. In addition to the complex network structures, nodes may have rich non structure information such as labels and contents. Therefore, structure, label and content constitute different aspects of the entire network system that reflect node similarities from multiple complementary facets. This thesis focuses on multifaceted network embedding learning, which aims to efficiently incorporate distinct aspects of information such as node labels and node contents for cooperative low-dimensional representation learning together with node topology.
Identifier: FA00013516 (IID)
Degree granted: Dissertation (Ph.D.)--Florida Atlantic University, 2020.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Embedded computer systems
Neural networks (Computer science)
Network embedding
Machine learning
Held by: Florida Atlantic University Libraries
Sublocation: Digital Library
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00013516
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.