Local Information and Structures in Analysis and Modelling of Complex Networks

Jia, Mingshan

Local Information and Structures in Analysis and Modelling of Complex Networks

Jia, Mingshan

Permalink

Publication Type:: Thesis
Issue Date:: 2022

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download contents and abstractAdobe PDF (224.61 kB)

Adobe PDF

Download thesisAdobe PDF (2.7 MB)

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Jia, Mingshan
dc.date.accessioned	2022-11-25T03:55:53Z
dc.date.available	2022-11-25T03:55:53Z
dc.date.issued	2022
dc.identifier.uri	http://hdl.handle.net/10453/163745
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	Abstracting entities and their interactions as nodes and links, networks are a general representation for modelling and studying complex systems. Modelling relational structures of the underlying data, rather than only a set of isolated entities, allows us to build more accurate models for various types of domain data, such as social relationships, molecular interactions, program executions, and many more. Despite being powerful and ubiquitous, networks are also difficult to process, mainly due to their complex topological structures. Therefore, the study of network structure, especially local structure, has been the core theme of studying complex networks. This dissertation aims to provide new understandings of how local structure information is extracted and utilised in studying different types of complex networks. The dissertation includes three original works in the direction of local structure and information on top of a comprehensive survey. In the review, we propose new taxonomies for graph structures that bring together the notions of centrality measures, motifs, and other local-level metrics. For theoretical understanding, we propose new metrics to quantify the formation of 3-node and 4-node subgraphs and develop new motif patterns that are distinctive features in both network- and node-level analysis. For methodological approaches, we propose the framework to effectively encode edge attributes into the typed-edge graphlet degree vector, for both sociocentric and egocentric networks. Moreover, for practical applications, the proposed metrics and approaches are applied in many different types of complex networks and case studies. They are not only proven to be effective in multiple learning and analytical tasks but also lead to new insights and interesting discoveries.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/163745/2/02whiole.pdf
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	au.edu.uts.lib/ppc
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Local Information and Structures in Analysis and Modelling of Complex Networks	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

Abstracting entities and their interactions as nodes and links, networks are a general representation for modelling and studying complex systems. Modelling relational structures of the underlying data, rather than only a set of isolated entities, allows us to build more accurate models for various types of domain data, such as social relationships, molecular interactions, program executions, and many more. Despite being powerful and ubiquitous, networks are also difficult to process, mainly due to their complex topological structures. Therefore, the study of network structure, especially local structure, has been the core theme of studying complex networks. This dissertation aims to provide new understandings of how local structure information is extracted and utilised in studying different types of complex networks. The dissertation includes three original works in the direction of local structure and information on top of a comprehensive survey. In the review, we propose new taxonomies for graph structures that bring together the notions of centrality measures, motifs, and other local-level metrics. For theoretical understanding, we propose new metrics to quantify the formation of 3-node and 4-node subgraphs and develop new motif patterns that are distinctive features in both network- and node-level analysis. For methodological approaches, we propose the framework to effectively encode edge attributes into the typed-edge graphlet degree vector, for both sociocentric and egocentric networks. Moreover, for practical applications, the proposed metrics and approaches are applied in many different types of complex networks and case studies. They are not only proven to be effective in multiple learning and analytical tasks but also lead to new insights and interesting discoveries.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/163745