MULTIFACETED EMBEDDING LEARNING FOR NETWORKED DATA AND SYSTEMS

• Other Authors: Shi, Min (author)
• Format: Others
• Language: English
• Published: Florida Atlantic University
• Subjects: Embedded computer systems; Neural networks (Computer science); Network embedding; Machine learning
• Online Access: http://purl.flvc.org/fau/fd/FA00013516

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. === Includes bibliography. === Dissertation (Ph.D.)--Florida Atlantic University, 2020. === FAU Electronic Theses and Dissertations Collection