# A Survey of Graph Unlearning arXiv: https://arxiv.org/abs/2310.02164 ยท HTML: https://mlsanity.com/paper/2310.02164/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Placed in Graph unlearning, one of 12 directions in LLM safety and security. - Builds on 59 of its 134 references that are in the map. - First posted on arXiv 23 Aug 2023. ## Placed in - LLM safety and security / [Graph unlearning](https://mlsanity.com/safety/graph-unlearning/) ## Builds on (most cited first, 30 of 59) - [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016) - [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017) - [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017) - [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018) - [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019) - [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016) - [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023) - [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020) - [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017) - [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018) - [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018) - [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019) - [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018) - [Certified Data Removal from Machine Learning Models](https://mlsanity.com/paper/1911.03030/) (2019) - [Fast Model Editing at Scale](https://mlsanity.com/paper/2110.11309/) (2021) - [Making AI Forget You: Data Deletion in Machine Learning](https://mlsanity.com/paper/1907.05012/) (2019) - [Remember What You Want to Forget: Algorithms for Machine Unlearning](https://mlsanity.com/paper/2103.03279/) (2021) - [Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://mlsanity.com/paper/1610.05755/) (2016) - [A Survey of Machine Unlearning](https://mlsanity.com/paper/2209.02299/) (2022) - [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017) - [On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning](https://mlsanity.com/paper/2110.11891/) (2021) - [A Survey on Graph Kernels](https://mlsanity.com/paper/1903.11835/) (2019) - [Graph Unlearning](https://mlsanity.com/paper/2103.14991/) (2021) - [Machine Unlearning via Algorithmic Stability](https://mlsanity.com/paper/2102.13179/) (2021) - [ETA Prediction with Graph Neural Networks in Google Maps](https://mlsanity.com/paper/2108.11482/) (2021) - [A Survey on The Expressive Power of Graph Neural Networks](https://mlsanity.com/paper/2003.04078/) (2020) - [Universal Prompt Tuning for Graph Neural Networks](https://mlsanity.com/paper/2209.15240/) (2022) - [Towards the Science of Security and Privacy in Machine Learning](https://mlsanity.com/paper/1611.03814/) (2016) - [Trustworthy Graph Neural Networks: Aspects, Methods and Trends](https://mlsanity.com/paper/2205.07424/) (2022) - [Membership Inference Attack on Graph Neural Networks](https://mlsanity.com/paper/2101.06570/) (2021) ## Built on by (most cited first, 12 of 12) - [Machine Unlearning: A Comprehensive Survey](https://mlsanity.com/paper/2405.07406/) (2024) - [Scalable and Certifiable Graph Unlearning: Overcoming the Approximation Error Barrier](https://mlsanity.com/paper/2408.09212/) (2024) - [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024) - [Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theory](https://mlsanity.com/paper/2405.03706/) (2024) - [Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks](https://mlsanity.com/paper/2407.19183/) (2024) - [Unlearning Inversion Attacks for Graph Neural Networks](https://mlsanity.com/paper/2506.00808/) (2025) - [A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks](https://mlsanity.com/paper/2412.00789/) (2024) - [GDGU: A Gradient Difference-based Graph Unlearning Method for Cyberattack Localization in Electric Vehicle Charging Networks](https://mlsanity.com/paper/2606.19566/) (2026) - [Targeted Therapy in Data Removal: Object Unlearning Based on Scene Graphs](https://mlsanity.com/paper/2412.00067/) (2024) - [Unlearning of Knowledge Graph Embedding via Preference Optimization](https://mlsanity.com/paper/2507.20566/) (2025) - [GraphToxin: Reconstructing Full Unlearned Graphs from Graph Unlearning](https://mlsanity.com/paper/2511.10936/) (2025) - [CUNO: Curriculum and Preference Optimization for Stable Graph Unlearning under Mass Deletion](https://mlsanity.com/paper/2609.08244/) (2026) ## Abstract (arXiv) 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 textitright 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. 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, recommender systems, and resource-constrained environments like the Internet of Things, 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.