Version 1
: Received: 23 September 2024 / Approved: 23 September 2024 / Online: 23 September 2024 (10:41:15 CEST)
How to cite:
Qi, C. Graph Unlearning: Mechanism and Future Direction for Machine Unlearning with Complex Relationships. Preprints2024, 2024091733. https://doi.org/10.20944/preprints202409.1733.v1
Qi, C. Graph Unlearning: Mechanism and Future Direction for Machine Unlearning with Complex Relationships. Preprints 2024, 2024091733. https://doi.org/10.20944/preprints202409.1733.v1
Qi, C. Graph Unlearning: Mechanism and Future Direction for Machine Unlearning with Complex Relationships. Preprints2024, 2024091733. https://doi.org/10.20944/preprints202409.1733.v1
APA Style
Qi, C. (2024). Graph Unlearning: Mechanism and Future Direction for Machine Unlearning with Complex Relationships. Preprints. https://doi.org/10.20944/preprints202409.1733.v1
Chicago/Turabian Style
Qi, C. 2024 "Graph Unlearning: Mechanism and Future Direction for Machine Unlearning with Complex Relationships" Preprints. https://doi.org/10.20944/preprints202409.1733.v1
Abstract
Graph unlearning has emerged as a crucial technique in privacy-preserving applications, particularly in scenarios where sensitive data must be erased from graph-based systems. This paper explores the fundamental concepts, challenges, and methodologies associated with graph unlearning, including its application to domains such as social networks, financial transaction systems, and healthcare networks. By delving into various algorithms, including local and global unlearning methods, we analyze the trade-offs between accuracy, privacy, and computational efficiency. The paper further investigates the security risks associated with graph unlearning, including adversarial and inference attacks, and proposes mitigation strategies to safeguard unlearning processes. We also examine the unique challenges posed by federated learning systems, where unlearning requires coordination across decentralized clients. Finally, the paper evaluates graph unlearning techniques using performance metrics such as time complexity, accuracy, and privacy guarantees, supported by real-world examples from diverse applications. The findings emphasize the importance of developing robust, scalable, and secure unlearning mechanisms to ensure data privacy and compliance with regulations. Evaluation and metrics for Graph Unlearning will also be discussed in this paper.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
