Anomaly Detection Techniques as a Quality Evaluation of graphs

dc.rights.license	CC-BY-NC-ND
dc.contributor.advisor	Velegrakis, Ioannis
dc.contributor.author	Lagunas, Luca
dc.date.accessioned	2022-09-09T00:03:18Z
dc.date.available	2022-09-09T00:03:18Z
dc.date.issued	2022
dc.identifier.uri	https://studenttheses.uu.nl/handle/20.500.12932/42423
dc.description.abstract	The goal of this project is the implementation of PyGQE, a software package that given a graph measures its quality by measuring the possible anomaly detections. The aim of this application is to help data scientists evaluate how important a dataset in graph form is and its level of quality. The program is implemented in python, it takes a list of edges in CSV format and a feature map (optional) and returns a list of anomalous nodes and uncommon features patterns.
dc.description.sponsorship	Utrecht University
dc.language.iso	EN
dc.subject	Anomaly Detection Techniques as a Quality Evaluation of graphs
dc.title	Anomaly Detection Techniques as a Quality Evaluation of graphs
dc.type.content	Master Thesis
dc.rights.accessrights	Open Access
dc.subject.keywords	graph;anomaly;anomalies;outlier,deep learning
dc.subject.courseuu	Applied Data Science
dc.thesis.id	8922