# Sybil Detection using Graph Neural Networks

arXiv: https://arxiv.org/abs/2409.08631 · HTML: https://mlsanity.com/paper/2409.08631/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [SATAR: A Self-supervised Approach to Twitter Account Representation Learning and its Application in Bot Detection](https://mlsanity.com/paper/2106.13089/) (2021)
- [Little Ball of Fur: A Python Library for Graph Sampling](https://mlsanity.com/paper/2006.04311/) (2020)
- [SybilBelief: A Semi-supervised Learning Approach for Structure-based Sybil Detection](https://mlsanity.com/paper/1312.5035/) (2013)
- [Structure-based Sybil Detection in Social Networks via Local Rule-based Propagation](https://mlsanity.com/paper/1803.04321/) (2018)
- [SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection](https://mlsanity.com/paper/1803.06772/) (2018)
- [SybilFrame: A Defense-in-Depth Framework for Structure-Based Sybil Detection](https://mlsanity.com/paper/1503.02985/) (2015)
- [SybilFence: Improving Social-Graph-Based Sybil Defenses with User Negative Feedback](https://mlsanity.com/paper/1304.3819/) (2013)

## Built on by (most cited first, 1 of 1)

- [Graph-based Fake Account Detection: A Survey](https://mlsanity.com/paper/2507.06541/) (2025)

## Abstract (arXiv)

This paper presents SYBILGAT, a novel approach to Sybil detection in social networks using Graph Attention Networks (GATs). Traditional methods for Sybil detection primarily leverage structural properties of networks; however, they tend to struggle with a large number of attack edges and are often unable to simultaneously utilize both known Sybil and honest nodes. Our proposed method addresses these limitations by dynamically assigning attention weights to different nodes during aggregations, enhancing detection performance. We conducted extensive experiments in various scenarios, including pretraining in sampled subgraphs, synthetic networks, and networks under targeted attacks. The results show that SYBILGAT significantly outperforms the state-of-the-art algorithms, particularly in scenarios with high attack complexity and when the number of attack edges increases. Our approach shows robust performance across different network models and sizes, even as the detection task becomes more challenging. We successfully applied the model to a real-world Twitter graph with more than 269k nodes and 6.8M edges. The flexibility and generalizability of SYBILGAT make it a promising tool to defend against Sybil attacks in online social networks with only structural information.