Stability of Graph Neural Network with respect to different types of topological perturbations

Bachelor thesis (2024)

Authors

A.R. Brown Electrical Engineering, Mathematics and Computer Science

Contributors

E. Isufi (mentor)
M. Sabbaqi (mentor)
Maosheng Yang (mentor)
K.A. Hildebrandt Computer Graphics and Visualisation - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science
More Info
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Published Date

27-06-2024

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Graph Neural Networks are widely used as useful tools to investigate graphs because they can learn from the topological structure of graphs. In practical applications, the graph’s structure can change over time, have errors or be subject to adversarial attacks. These perturbations negatively impact the accuracy of the neural network. The theoretical stability of graph neural networks has been analysed already and in this paper, the stability of graph neural networks is investigated experimentally. The performance of different perturbation strategies is compared to see how different perturbations impact stability.

Files

Research_paper.pdf
(.pdf | 0.494 Mb)