A Deep Generative Model for Graph-Structured Data

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Sarshar Tehrani, Fatemeh | 2022

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 55709 (19)
University: Sharif University of Technology
Department: Computer Engineering
Advisor(s): Movaghar, Ali
Abstract:
In recent years, deep generative models have achieved incredible successes in various fields, including graph generation. Due to the advances made in graph generation by deep generative models, these methods have shown numerous applications from drug discovery and molecular graph generation to modeling social and citation network graphs. Graph generation is an approach to discovering and exploring new graph structures and has been attracting growing attention. One of the most challenging applications of deep graph generative models is molecular graph generation since it requires not only generating chemically valid molecular structures but also optimizing their chemical properties in the meantime. Therefore, we aim to find a deep graph generation architecture that is capable of generating valid molecules the possibility of optimization for the properties desired by researchers using generative adversarial networks and flow generative models.
Keywords:
Deep Generative Modeling ; Deep Learning ; Flow Generative Models ; Deep Graph Generation ; Molecular Graph Generation