Graph Unlearning for MLaaS: Toward Flexible Privacy Adjustment via Influenced Subgraph

Graph unlearning removes specific information from graph neural networks (GNNs, which are AI models that process data organized as networks of connected nodes). In machine learning-as-a-service (MLaaS, where companies host AI models for users to access), service providers usually cannot see the original training data, making existing unlearning methods impractical. This paper introduces SCGU (subgraph-based certified graph unlearning), a method that lets service providers directly modify model parameters to remove specific information without needing access to the training data, using only a smaller portion of the model related to what needs to be removed.