Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/579303
Title: Representation Learning from Time Varying Networks and its Application to Temporal Link Prediction
Researcher: Mohan, Anuraj
Guide(s): Rajeev, D
Keywords: Computer Science
Computer Science Interdisciplinary Applications
Engineering and Technology
Graph Attention Network
Machine Learning
Network Embedding
Network Representation Learning
Temporal Network Embedding
University: Cochin University of Science and Technology
Completed Date: 2021
Abstract: Machine learning with graph-structured data has gained broad research interest in newlinerecent years due to the increased importance of performing network mining tasks newlineon data from various domains. Generating efficient network representation is one newlineimportant challenge in applying machine learning over network data. Recently, newlinerepresentation learning methods are widely used in various domains to generate newlinelow dimensional latent features from complex high dimensional data. A significant amount of research effort is made in the past to generate node representations newlinefrom graph-structured data using representation learning methods. Most of these newlinemethods are only applicable to static networks and therefore cannot capture the newlineevolving nature and temporal dynamics of time-varying networks. This research newlineaims to develop representation learning methods for two different dimensions of newlinetime-varying networks, namely dynamic networks and temporal networks. newline
Pagination: 179
URI: http://hdl.handle.net/10603/579303
Appears in Departments:Department of Computer Applications

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02_preliminary pages.pdf1.44 MBAdobe PDFView/Open
03_content.pdf340.63 kBAdobe PDFView/Open
04_abstract.pdf260.45 kBAdobe PDFView/Open
05_chapter 1.pdf1.81 MBAdobe PDFView/Open
06_chapter 2.pdf8.43 MBAdobe PDFView/Open
07_chapter 3.pdf4.97 MBAdobe PDFView/Open
08_ chapter 4.pdf3.76 MBAdobe PDFView/Open
09_chapter 5.pdf5 MBAdobe PDFView/Open
10_chapter 6.pdf664.37 kBAdobe PDFView/Open
80_recommendation.pdf101.73 kBAdobe PDFView/Open
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