McDonald, David ORCID: 0000-0002-0540-8254 (2021). The hierarchical organisation and dynamics of complex networks. University of Birmingham. Ph.D.
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McDonald2021PhD.pdf
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Abstract
Complex networks offer flexible representations of complex heterogeneous real-world systems. They are often weighted, attributed, directed and/or dynamic. As such, gaining an overall understanding of information flow through these systems remains a challenging problem in the machine learning community. This thesis provides a comprehensive examination of the hierarchy inherent to many complex networks, with the following contributions:
• The first algorithm to learn low dimensional non-Euclidean representations of attributed nodes in a weighted complex network.
• The first algorithm to learn low dimensional non-Euclidean representations of attributed nodes in a directed complex network.
• A framework to explore the multi-scale organization of meso-scopic architectures in signalling networks, allowing for the identification of statistically significant drug-able targets.
Through these contributions, the work proposed in this thesis contributes towards a greater understanding of the hierarchy in the organization and dynamics of complex real-world systems.
Type of Work: | Thesis (Doctorates > Ph.D.) | |||||||||
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Award Type: | Doctorates > Ph.D. | |||||||||
Supervisor(s): |
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Licence: | All rights reserved | |||||||||
College/Faculty: | Colleges (2008 onwards) > College of Engineering & Physical Sciences | |||||||||
School or Department: | School of Computer Science | |||||||||
Funders: | None/not applicable | |||||||||
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science | |||||||||
URI: | http://etheses.bham.ac.uk/id/eprint/11255 |
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