# GA Based Q-Attack on Community Detection

arXiv: https://arxiv.org/abs/1811.00430 · HTML: https://mlsanity.com/paper/1811.00430/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## Built on by (most cited first, 13 of 13)

- [Graph-Fraudster: Adversarial Attacks on Graph Neural Network Based Vertical Federated Learning](https://mlsanity.com/paper/2110.06468/) (2021)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [GANI: Global Attacks on Graph Neural Networks via Imperceptible Node Injections](https://mlsanity.com/paper/2210.12598/) (2022)
- [GraphAttacker: A General Multi-Task GraphAttack Framework](https://mlsanity.com/paper/2101.06855/) (2021)
- [Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning](https://mlsanity.com/paper/2305.02901/) (2023)
- [Can Large Language Models Be Trusted as Evolutionary Optimizers for Network-Structured Combinatorial Problems?](https://mlsanity.com/paper/2501.15081/) (2025)
- [Graphfool: Targeted Label Adversarial Attack on Graph Embedding](https://mlsanity.com/paper/2102.12284/) (2021)
- [Quantifying the Noise of Structural Perturbations on Graph Adversarial Attacks](https://mlsanity.com/paper/2504.20869/) (2025)
- [Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense](https://mlsanity.com/paper/2505.03424/) (2025)
- [Unnoticeable Community Deception via Multi-objective Optimization](https://mlsanity.com/paper/2509.01438/) (2025)
- [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)
- [Community Concealment from Graph Neural Networks](https://mlsanity.com/paper/2602.12250/) (2026)

## Abstract (arXiv)

Community detection plays an important role in social networks, since it can help to naturally divide the network into smaller parts so as to simplify network analysis. However, on the other hand, it arises the concern that individual information may be over-mined, and the concept community deception thus is proposed to protect individual privacy on social networks. Here, we introduce and formalize the problem of community detection attack and develop efficient strategies to attack community detection algorithms by rewiring a small number of connections, leading to individual privacy protection. In particular, we first give two heuristic attack strategies, i.e., Community Detection Attack (CDA) and Degree Based Attack (DBA), as baselines, utilizing the information of detected community structure and node degree, respectively. And then we propose a Genetic Algorithm (GA) based Q-Attack, where the modularity Q is used to design the fitness function. We launch community detection attack based on the above three strategies against three modularity based community detection algorithms on two social networks. By comparison, our Q-Attack method achieves much better attack effects than CDA and DBA, in terms of the larger reduction of both modularity Q and Normalized Mutual Information (NMI). Besides, we find that the adversarial networks obtained by Q-Attack on a specific community detection algorithm can be still effective on others, no matter whether they are modularity based or not, indicating its strong transferability.