A Survey of Graph Unlearning
- URL: http://arxiv.org/abs/2310.02164v2
- Date: Sat, 7 Oct 2023 19:50:17 GMT
- Title: A Survey of Graph Unlearning
- Authors: Anwar Said and Tyler Derr and Mudassir Shabbir and Waseem Abbas and
Xenofon Koutsoukos
- Abstract summary: Graph unlearning provides the means to remove sensitive data traces from trained models, thereby upholding the right to be forgotten.
We present the first systematic review of graph unlearning approaches, encompassing a diverse array of methodologies.
We explore the versatility of graph unlearning across various domains, including but not limited to social networks, adversarial settings, and resource-constrained environments.
- Score: 11.841882902141696
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Graph unlearning emerges as a crucial advancement in the pursuit of
responsible AI, providing the means to remove sensitive data traces from
trained models, thereby upholding the right to be forgotten. It is evident that
graph machine learning exhibits sensitivity to data privacy and adversarial
attacks, necessitating the application of graph unlearning techniques to
address these concerns effectively. In this comprehensive survey paper, we
present the first systematic review of graph unlearning approaches,
encompassing a diverse array of methodologies and offering a detailed taxonomy
and up-to-date literature overview to facilitate the understanding of
researchers new to this field. Additionally, we establish the vital connections
between graph unlearning and differential privacy, augmenting our understanding
of the relevance of privacy-preserving techniques in this context. To ensure
clarity, we provide lucid explanations of the fundamental concepts and
evaluation measures used in graph unlearning, catering to a broader audience
with varying levels of expertise. Delving into potential applications, we
explore the versatility of graph unlearning across various domains, including
but not limited to social networks, adversarial settings, and
resource-constrained environments like the Internet of Things (IoT),
illustrating its potential impact in safeguarding data privacy and enhancing AI
systems' robustness. Finally, we shed light on promising research directions,
encouraging further progress and innovation within the domain of graph
unlearning. By laying a solid foundation and fostering continued progress, this
survey seeks to inspire researchers to further advance the field of graph
unlearning, thereby instilling confidence in the ethical growth of AI systems
and reinforcing the responsible application of machine learning techniques in
various domains.
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