Modelling temporal networks with scientific machine learning.

Simple item page
dc.contributor.authorSmith, Connor
dc.date.accessioned2023-10-25T00:01:54Z
dc.date.available2023-10-25T00:01:54Z
dc.date.issued2023
dc.description.abstractTemporal networks appear in applications ranging from symptom prediction to social networks to epidemiology. This thesis proposes a flexible framework for modelling temporal network progression which utilises a combination of singular value decomposition, scientific machine learning, and symbolic regression. We demonstrate the usefulness of the framework for modelling the progression of a variety of small temporal networks. Additionally, we present a Julia programming package for streamlining work with temporal networks. This thesis demonstrates our framework as a proof of concept, and we are confident that with further research, this framework will be a useful tool for modelling temporal networks in many fields.
dc.identifier.urihttps://hdl.handle.net/10092/106300
dc.identifier.urihttps://doi.org/10.26021/15091
dc.languageEnglish
dc.language.isoen
dc.rightsAll Right Reserved
dc.rights.urihttps://canterbury.libguides.com/rights/theses
dc.titleModelling temporal networks with scientific machine learning.
dc.typeTheses / Dissertations
thesis.degree.disciplineMathematics
thesis.degree.grantorUniversity of Canterbury
thesis.degree.levelMasters
thesis.degree.nameOther
uc.bibnumber3340191
uc.collegeFaculty of Engineering
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
Smith, Connor_MMATHSCI thesis.pdf
Size:
484.08 KB
Format:
Adobe Portable Document Format
Download
No Thumbnail Available
Name:
Smith, Connor_Co-authorship-Form.docx
Size:
106.27 KB
Format:
Microsoft Word
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Engineering: Theses and Dissertations