Skip to main content
DORAS
DCU Online Research Access Service
Login (DCU Staff Only)
A methodology for automating graph construction and evaluation

Xing, Congcong (2021) A methodology for automating graph construction and evaluation. Master of Science thesis, Dublin City University.

Full text available as:

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
4MB

Abstract

Graphs and graph analytics facilitate new approaches to machine learning. They also provide the ability to extract new insights from the same datasets as used in traditional machine learning experiments. For this reason, many researchers are seeking to exploit graph databases in pursuit of better performance for their predictive models. However, the construction of a graph from relational or flat models such as CSV files is not a straightforward transformation. A careful selection of nodes and relationships is required to ensure an optimal construction of the target graph. Overly large graphs can cause performance issues for a number of graph algorithms and thus, graph compression is an important part of the construction process. This research has 2 components: the usage of graphs to integrate multiple data sources and a graph transformation methodology to create the integrated schema and populate the graph. Our approach to validation uses link prediction and community detection graph analytics to evaluate the graphs built using our methodology.

Item Type:Thesis (Master of Science)
Date of Award:November 2021
Refereed:No
Supervisor(s):Roantree, Mark and McCarren, Andrew
Subjects:Computer Science > Computer engineering
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 3.0 License. View License
Funders:SFI/12/RC/2289
ID Code:26209
Deposited On:27 Oct 2021 15:18 by Andrew Mccarren . Last Modified 27 Oct 2021 15:18

Downloads

Downloads per month over past year

Archive Staff Only: edit this record

Altmetric
- Altmetric
+ Altmetric
  • Student Email
  • Staff Email
  • Student Apps
  • Staff Apps
  • Loop
  • Disclaimer
  • Privacy
  • Contact Us