# Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks

arXiv: https://arxiv.org/abs/2004.04834 · HTML: https://mlsanity.com/paper/2004.04834/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2020.

## Built on by (most cited first, 19 of 19)

- [Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods](https://mlsanity.com/paper/2110.14446/) (2021)
- [The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention](https://mlsanity.com/paper/2307.11864/) (2023)
- [RoSGAS: Adaptive Social Bot Detection with Reinforced Self-Supervised GNN Architecture Search](https://mlsanity.com/paper/2206.06757/) (2022)
- [Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation](https://mlsanity.com/paper/2208.09779/) (2022)
- [SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning](https://mlsanity.com/paper/2406.11389/) (2024)
- [DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector](https://mlsanity.com/paper/2410.06549/) (2024)
- [Simplifying Distributed Neural Network Training on Massive Graphs: Randomized Partitions Improve Model Aggregation](https://mlsanity.com/paper/2305.09887/) (2023)
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- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [Robust Node Representation Learning via Graph Variational Diffusion Networks](https://mlsanity.com/paper/2312.10903/) (2023)
- [KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Data](https://mlsanity.com/paper/2410.08390/) (2024)
- [Preemptive Detection of Fake Accounts on Social Networks via Multi-Class Preferential Attachment Classifiers](https://mlsanity.com/paper/2308.05353/) (2023)
- [RoBCtrl: Attacking GNN-Based Social Bot Detectors via Reinforced Manipulation of Bots Control Interaction](https://mlsanity.com/paper/2510.16035/) (2025)
- [BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection](https://mlsanity.com/paper/2506.13795/) (2025)
- [Graph-based Fake Account Detection: A Survey](https://mlsanity.com/paper/2507.06541/) (2025)
- [Representation Learning of Complex Assemblies, An Effort to Improve Corporate Scope 3 Emissions Calculation](https://mlsanity.com/paper/2409.03769/) (2024)
- [Weak Links in LinkedIn: Enhancing Fake Profile Detection in the Age of LLMs](https://mlsanity.com/paper/2507.16860/) (2025)
- [Explainable Fraud Detection with GNNExplainer and Shapley Values](https://mlsanity.com/paper/2509.12262/) (2025)
- [Are Large Language Models Suitable for Graph Computation? Progress and Prospects](https://mlsanity.com/paper/2606.06865/) (2026)

## Abstract (arXiv)

In this paper, we study the problem of early detection of fake user accounts on social networks based solely on their network connectivity with other users. Removing such accounts is a core task for maintaining the integrity of social networks, and early detection helps to reduce the harm that such accounts inflict. However, new fake accounts are notoriously difficult to detect via graph-based algorithms, as their small number of connections are unlikely to reflect a significant structural difference from those of new real accounts. We present the SybilEdge algorithm, which determines whether a new user is a fake account (`sybil') by aggregating over (I) her choices of friend request targets and (II) these targets' respective responses. SybilEdge performs this aggregation giving more weight to a user's choices of targets to the extent that these targets are preferred by other fakes versus real users, and also to the extent that these targets respond differently to fakes versus real users. We show that SybilEdge rapidly detects new fake users at scale on the Facebook network and outperforms state-of-the-art algorithms. We also show that SybilEdge is robust to label noise in the training data, to different prevalences of fake accounts in the network, and to several different ways fakes can select targets for their friend requests. To our knowledge, this is the first time a graph-based algorithm has been shown to achieve high performance (AUC>0.9) on new users who have only sent a small number of friend requests.