all InfoSec news
Graph Unlearning with Efficient Partial Retraining
March 13, 2024, 4:11 a.m. | Jiahao Zhang, Lin Wang, Shijie Wang, Wenqi Fan
cs.CR updates on arXiv.org arxiv.org
Abstract: Graph Neural Networks (GNNs) have achieved remarkable success in various real-world applications. However, GNNs may be trained on undesirable graph data, which can degrade their performance and reliability. To enable trained GNNs to efficiently unlearn unwanted data, a desirable solution is retraining-based graph unlearning, which partitions the training graph into subgraphs and trains sub-models on them, allowing fast unlearning through partial retraining. However, the graph partition process causes information loss in the training graph, resulting …
applications arxiv can cs.cr cs.lg data enable graph may networks neural networks partial performance real reliability solution training world
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
SOC 2 Manager, Audit and Certification
@ Deloitte | US and CA Multiple Locations
Information Security Engineer - Vulnerability Management
@ Starling Bank | Southampton, England, United Kingdom
Manager Cybersecurity
@ Sia Partners | Rotterdam, Netherlands
Compliance Analyst
@ SiteMinder | Manila
Information System Security Engineer (ISSE)-Level 3, OS&CI Job #447
@ Allen Integrated Solutions | Chantilly, Virginia, United States
Enterprise Cyber Security Analyst – Advisory and Consulting
@ Ford Motor Company | Mexico City, MEX, Mexico