TabHGIF: A Unified Hypergraph Influence Framework for Efficient Unlearning in Tabular Data

Summary

TabHGIF is a hypergraph-aware unlearning framework for tabular data that represents rows and attributes as a row-attribute hypergraph to capture high-order feature correlations. It introduces a Hypergraph Influence Function (HGIF) to approximate parameter shifts from structural edits, enabling one-shot parameter correction without re-accessing the raw forget samples. On three benchmarks and four hypergraph backbones, it reaches post-unlearning accuracy close to full retraining, with 2.18–4.27× speedups for row unlearning and 5.05–7.67× for column unlearning, and MIA-AUC values of 0.5014–0.5464.