The hierarchical organisation and dynamics of complex networks

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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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.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):