Anomaly detection in multiplex networks : from human brain activity to financial networks
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Behrouz, Ali
issued: 2023-08-17T22:46:47Z
issued: 2023
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: The problem of identifying anomalies in dynamic networks is a fundamental task with a wide range of applications from understanding brain diseases/disorders to fraud detection in financial networks. However, it raises critical challenges due to the complex nature of anomalies, lack of ground truth knowledge, and complex and dynamic interactions in the network. Most existing approaches usually study simple networks with a single type of connection. However many complex systems exhibit natural relationships with different types of connections, yielding multiplex networks. We first propose ANOMULY, a graph neural network-based unsupervised edge anomaly detection framework for multiplex dynamic networks. ANOMULY learns node encodings in different relation types, separately, and then uses an attention mechanism that incorporates information across different types of relations. To improve generalizability and scalability, we further propose ADMIRE, an inductive and unsupervised anomaly detection method that extracts the causality of the existence of connections by temporal network motifs. To extract the temporal network motifs, ADMIRE uses two different casual multiplex walks, inter-view and intra-view that automatically extract and learn temporal multiplex network motifs. Despite the outstanding performance of ADMIRE, using it in sensitive decision-making tasks requires explanations for the model's predictions. Accordingly, we introduce an interpretable, weighted optimal sparse decision tree model, ADMIRE++, that mimics ADMIRE, to provide explanations for ADMIRE's predictions. With extensive experiments, we show the efficiency and effectiveness of our approaches in detecting anomalous connections in various domains, including social and financial networks. We further focus on understanding abnormal human brain activity of people living with Parkinson’s Disease, Attention Deficit Hyperactivity Disorder, and Autism Spectrum Disorder to show how these methods can assist in …
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/85514
note: Anomaly Detection in Multiplex Networks: From HumanBrain Activity to Financial NetworksbyAli BehrouzB.Sc. Computer Engineering, Sharif University of Technology, 2020A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMaster of ScienceinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Computer Science)The University of British Columbia(Vancouver)August 2023© Ali Behrouz, 2023The following individuals certify that they have read, and recommend to the Facultyof Graduate and Postdoctoral Studies for acceptance, the thesis entitled:Anomaly Detection in Multiplex Networks: From Human Brain Activityto Financial Networkssubmitted by Ali Behrouz in partial fulfillment of the requirements for the degreeof Master of Science in Computer Science.Examining Committee:Margo Seltzer, Professor, Computer Science, UBCSupervisorLaks V.S. Lakshmanan, Professor, Computer Science, UBCSupervisory CommitteeiiAbstractThe problem of identifying anomalies in dynamic networks is a fundamental taskwith a wide range of applications from understanding brain diseases/disorders tofraud detection in financial networks. However, it raises critical challenges dueto the complex nature of anomalies, lack of ground truth knowledge, and com-plex and dynamic interactions in the network. Most existing approaches usuallystudy simple networks with a single type of connection. However many complexsystems exhibit natural relationships with different types of connections, yieldingmultiplex networks. We first propose ANOMULY, a graph neural network-basedunsupervised edge anomaly detection framework for multiplex dynamic networks.ANOMULY learns node encodings in different relation types, separately, and thenuses an attention mechanism that incorporates information across different typesof relations. To improve generalizability and scalability, we further propose AD-MIRE, an inductive and unsupervised anomaly detection method that extracts thecausality of the existence of connections by tem…
has type: Thesis/Dissertation
date issued: 2023-11
is shown at: 10.14288/1.0435270
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Seltzer, Margo
type: Text
identifier URI: http://hdl.handle.net/2429/85514