# Evading Community Detection via Counterfactual Neighborhood Search

arXiv: https://arxiv.org/abs/2310.08909 · HTML: https://mlsanity.com/paper/2310.08909/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 44 references that are in the map.
- First posted on arXiv 13 Oct 2023.

## Builds on (most cited first, 10 of 10)

- [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)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Hiding Individuals and Communities in a Social Network](https://mlsanity.com/paper/1608.00375/) (2016)
- [Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking](https://mlsanity.com/paper/1706.06691/) (2017)
- [A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2101.01669/) (2021)
- [Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems](https://mlsanity.com/paper/2104.11434/) (2021)
- [ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models](https://mlsanity.com/paper/2110.11960/) (2021)
- [Overlapping Community Detection Using Neighborhood-Inflated Seed Expansion](https://mlsanity.com/paper/1503.07439/) (2015)
- [Hide and Seek: Outwitting Community Detection Algorithms](https://mlsanity.com/paper/2102.10759/) (2021)

## Built on by (most cited first, 2 of 2)

- [Evading Overlapping Community Detection via Proxy Node Injection](https://mlsanity.com/paper/2509.21211/) (2025)
- [Attacking and Securing Community Detection: A Game-Theoretic Framework](https://mlsanity.com/paper/2512.11359/) (2025)

## Abstract (arXiv)

Community detection techniques are useful for social media platforms to discover tightly connected groups of users who share common interests. However, this functionality often comes at the expense of potentially exposing individuals to privacy breaches by inadvertently revealing their tastes or preferences. Therefore, some users may wish to preserve their anonymity and opt out of community detection for various reasons, such as affiliation with political or religious organizations, without leaving the platform. In this study, we address the challenge of community membership hiding, which involves strategically altering the structural properties of a network graph to prevent one or more nodes from being identified by a given community detection algorithm. We tackle this problem by formulating it as a constrained counterfactual graph objective, and we solve it via deep reinforcement learning. Extensive experiments demonstrate that our method outperforms existing baselines, striking the best balance between accuracy and cost.