# Unnoticeable Community Deception via Multi-objective Optimization

arXiv: https://arxiv.org/abs/2509.01438 · HTML: https://mlsanity.com/paper/2509.01438/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 56 references that are in the map.
- First posted on arXiv 1 Sep 2025.

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

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Cooperative Game Theory Approaches for Network Partitioning](https://mlsanity.com/paper/1707.03587/) (2017)
- [Hiding Individuals and Communities in a Social Network](https://mlsanity.com/paper/1608.00375/) (2016)
- [Adversarial Attack on Community Detection by Hiding Individuals](https://mlsanity.com/paper/2001.07933/) (2020)
- [DeepTrax: Embedding Graphs of Financial Transactions](https://mlsanity.com/paper/1907.07225/) (2019)
- [GA Based Q-Attack on Community Detection](https://mlsanity.com/paper/1811.00430/) (2018)
- [Multiscale Evolutionary Perturbation Attack on Community Detection](https://mlsanity.com/paper/1910.09741/) (2019)
- [Improving PageRank for Local Community Detection](https://mlsanity.com/paper/1610.08722/) (2016)
- [Clique percolation method: memory efficient almost exact communities](https://mlsanity.com/paper/2110.01213/) (2021)

## Abstract (arXiv)

Community detection in graphs is crucial for understanding the organization of nodes into densely connected clusters. While numerous strategies have been developed to identify these clusters, the success of community detection can lead to privacy and information security concerns, as individuals may not want their personal information exposed. To address this, community deception methods have been proposed to reduce the effectiveness of detection algorithms. Nevertheless, several limitations, such as the rationality of evaluation metrics and the unnoticeability of attacks, have been ignored in current deception methods. Therefore, in this work, we first investigate the limitations of the widely used deception metric, i.e., the decrease of modularity, through empirical studies. Then, we propose a new deception metric, and combine this new metric together with the attack budget to model the unnoticeable community deception task as a multi-objective optimization problem. To further improve the deception performance, we propose two variant methods by incorporating the degree-biased and community-biased candidate node selection mechanisms. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed community deception strategies.