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arXiv Machine Learning By Dalyapraz Manatova, Pablo Moriano, L. Jean Camp

Community Concealment from Graph Neural Networks

Read the original on arXiv Machine Learning →

The paper introduces FCom‑DICE, a feature‑aware perturbation method that rewires influential edges and adjusts node features to hide a target community from graph neural network (GNN) inference. It shows that concealment effectiveness depends on boundary connectivity and feature similarity, and that FCom‑DICE outperforms structure‑only DICE on synthetic and real networks such as Facebook, Wikipedia, and Bitcoin Transactions while preserving key structural and feature properties.

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