# Adversarial Attack on Community Detection by Hiding Individuals

arXiv: https://arxiv.org/abs/2001.07933 · HTML: https://mlsanity.com/paper/2001.07933/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2020.

## Built on by (most cited first, 20 of 20)

- [How does Heterophily Impact the Robustness of Graph Neural Networks? Theoretical Connections and Practical Implications](https://mlsanity.com/paper/2106.07767/) (2021)
- [Generative Adversarial Networks: A Survey Towards Private and Secure Applications](https://mlsanity.com/paper/2106.03785/) (2021)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [Adversarial Attack Framework on Graph Embedding Models with Limited Knowledge](https://mlsanity.com/paper/2105.12419/) (2021)
- [Mask-GVAE: Blind Denoising Graphs via Partition](https://mlsanity.com/paper/2102.04228/) (2021)
- [Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees](https://mlsanity.com/paper/2205.03546/) (2022)
- [Neighboring Backdoor Attacks on Graph Convolutional Network](https://mlsanity.com/paper/2201.06202/) (2022)
- [FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification](https://mlsanity.com/paper/2210.13815/) (2022)
- [Task and Model Agnostic Adversarial Attack on Graph Neural Networks](https://mlsanity.com/paper/2112.13267/) (2021)
- [An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework](https://mlsanity.com/paper/2204.13172/) (2022)
- [Rethinking Independent Cross-Entropy Loss For Graph-Structured Data](https://mlsanity.com/paper/2405.15564/) (2024)
- [Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks](https://mlsanity.com/paper/2110.10482/) (2021)
- [Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning](https://mlsanity.com/paper/2305.02901/) (2023)
- [AMNCutter: Affinity-Attention-Guided Multi-View Normalized Cutter for Unsupervised Surgical Instrument Segmentation](https://mlsanity.com/paper/2411.03695/) (2024)
- [Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation](https://mlsanity.com/paper/2211.08068/) (2022)
- [Expander Hierarchies for Normalized Cuts on Graphs](https://mlsanity.com/paper/2406.14111/) (2024)
- [Unnoticeable Community Deception via Multi-objective Optimization](https://mlsanity.com/paper/2509.01438/) (2025)
- [LoReTTA: A Low Resource Framework To Poison Continuous Time Dynamic Graphs](https://mlsanity.com/paper/2511.07379/) (2025)
- [Attacking and Securing Community Detection: A Game-Theoretic Framework](https://mlsanity.com/paper/2512.11359/) (2025)
- [Community Concealment from Graph Neural Networks](https://mlsanity.com/paper/2602.12250/) (2026)

## Abstract (arXiv)

It has been demonstrated that adversarial graphs, i.e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks. In this paper, we extend adversarial graphs to the problem of community detection which is much more difficult. We focus on black-box attack and aim to hide targeted individuals from the detection of deep graph community detection models, which has many applications in real-world scenarios, for example, protecting personal privacy in social networks and understanding camouflage patterns in transaction networks. We propose an iterative learning framework that takes turns to update two modules: one working as the constrained graph generator and the other as the surrogate community detection model. We also find that the adversarial graphs generated by our method can be transferred to other learning based community detection models.