# Evading Overlapping Community Detection via Proxy Node Injection

arXiv: https://arxiv.org/abs/2509.21211 · HTML: https://mlsanity.com/paper/2509.21211/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 54 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Builds on (most cited first, 21 of 21)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [Adversarial Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/1805.07984/) (2018)
- [Adversarial Attack on Graph Structured Data](https://mlsanity.com/paper/1806.02371/) (2018)
- [PairNorm: Tackling Oversmoothing in GNNs](https://mlsanity.com/paper/1909.12223/) (2019)
- [Action Branching Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1711.08946/) (2017)
- [Hiding Individuals and Communities in a Social Network](https://mlsanity.com/paper/1608.00375/) (2016)
- [Attack Graph Convolutional Networks by Adding Fake Nodes](https://mlsanity.com/paper/1810.10751/) (2018)
- [GA Based Q-Attack on Community Detection](https://mlsanity.com/paper/1811.00430/) (2018)
- [Practical Attacks Against Graph-based Clustering](https://mlsanity.com/paper/1708.09056/) (2017)
- [Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors](https://mlsanity.com/paper/2109.13069/) (2021)
- [Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion](https://mlsanity.com/paper/1503.07439/) (2015)
- [Sparse Vicious Attacks on Graph Neural Networks](https://mlsanity.com/paper/2209.09688/) (2022)
- [Evading Community Detection via Counterfactual Neighborhood Search](https://mlsanity.com/paper/2310.08909/) (2023)
- [The Right to Hide: Masking Community Affiliation via Minimal Graph Rewiring](https://mlsanity.com/paper/2502.00432/) (2025)

## Abstract (arXiv)

Protecting privacy in social graphs requires preventing sensitive information, such as community affiliations, from being inferred by graph analysis, without substantially altering the graph topology. We address this through the problem of community membership hiding (CMH), which seeks edge modifications that cause a target node to exit its original community, regardless of the detection algorithm employed. Prior work has focused on non-overlapping community detection, where trivial strategies often suffice, but real-world graphs are better modeled by overlapping communities, where such strategies fail. To the best of our knowledge, we are the first to formalize and address CMH in this setting. In this work, we propose a deep reinforcement learning (DRL) approach that learns effective modification policies, including the use of proxy nodes, while preserving graph structure. Experiments on real-world datasets show that our method significantly outperforms existing baselines in both effectiveness and efficiency, offering a principled tool for privacy-preserving graph modification with overlapping communities.