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Learning with Attributed Networks: Algorithms and Applications

Abstract Attributes - that delineating the properties of data, and connections - that describing the dependencies of data, are two essential components to characterize most real-world phenomena. The synergy between these two principal elements renders a unique data representation - the attributed networks. In many cases, people are inundated with vast amounts of data that can be structured into attributed networks, and their use has been attractive to researchers and practitioners in different disciplines. For example, in social media, users interact with each other and also post personalized content; in scientific collaboration, researchers cooperate and are distinct from peers by their unique research interests; in complex diseases studies, rich g... (more)
Created Date 2019
Contributor Li, Jundong (Author) / Liu, Huan (Advisor) / Faloutsos, Christos (Committee member) / He, Jingrui (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Subject Computer science / Applications / Attributed Networks / Feature Selection / Network Embedding / Online Algorithms
Type Doctoral Dissertation
Extent 162 pages
Language English
Note Doctoral Dissertation Computer Science 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis