Thesis etd-03122024-153814 |
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Thesis type
Tesi di laurea magistrale
URN
etd-03122024-153814
Thesis title
Graph Learning for Network Quantification
Department
INFORMATICA
Course of study
INFORMATICA
Supervisors
.
relatore Prof. Micheli, Alessio
relatore Dott. Sebastiani, Fabrizio
relatore Dott. Podda, Marco
relatore Dott. Sebastiani, Fabrizio
relatore Dott. Podda, Marco
Keywords
- Graph Learning
- Network Quantification
Graduation session start date
12/04/2024
Availability
Withheld
Release date
12/04/2094
Abstract (Inglese)
Abstract (Italiano)
Among the various challenges that machine learning addresses, this thesis focuses on the problem of graph quantification. Quantification is the task of estimating the class prevalence values (or
class priors) within a sample of unlabelled data points that exhibit Dataset Shift. Graph (or Network) quantification extends this task to the graph setting, which is characterized by data points that are interconnected with each other.
The thesis analyzes the impact of quantification methods and graph learning methods in the context of graph quantification. Moreover, it provides a new state-of-the-art method, GESN-SLD, that outperforms all the previously presented methods in the literature.
class priors) within a sample of unlabelled data points that exhibit Dataset Shift. Graph (or Network) quantification extends this task to the graph setting, which is characterized by data points that are interconnected with each other.
The thesis analyzes the impact of quantification methods and graph learning methods in the context of graph quantification. Moreover, it provides a new state-of-the-art method, GESN-SLD, that outperforms all the previously presented methods in the literature.
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