# Anomaly detection in online social networks

arXiv: https://arxiv.org/abs/1608.00301 · HTML: https://mlsanity.com/paper/1608.00301/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2016.

## Built on by (most cited first, 15 of 15)

- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities](https://mlsanity.com/paper/2302.01018/) (2023)
- [GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction](https://mlsanity.com/paper/2306.01951/) (2023)
- [A Data-Driven Approach to Robust Hypothesis Testing Using Sinkhorn Uncertainty Sets](https://mlsanity.com/paper/2202.04258/) (2022)
- [METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection](https://mlsanity.com/paper/2312.16831/) (2023)
- [TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models](https://mlsanity.com/paper/2307.12336/) (2023)
- [Graph Evidential Learning for Anomaly Detection](https://mlsanity.com/paper/2506.00594/) (2025)
- [Anomaly Detection in Graph Structured Data: A Survey](https://mlsanity.com/paper/2405.06172/) (2024)
- [Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection](https://mlsanity.com/paper/2110.14729/) (2021)
- [GRASPED: Graph Anomaly Detection using Autoencoder with Spectral Encoder and Decoder (Full Version)](https://mlsanity.com/paper/2508.15633/) (2025)
- [Efficient pattern-based anomaly detection in a network of multivariate devices](https://mlsanity.com/paper/2305.05538/) (2023)
- [Foundations for Unfairness in Anomaly Detection -- Case Studies in Facial Imaging Data](https://mlsanity.com/paper/2407.19646/) (2024)
- [A Review of Artificial Intelligence Impacting Statistical Process Monitoring and Future Directions](https://mlsanity.com/paper/2503.01858/) (2025)
- [EvoFormer: Learning Dynamic Graph-Level Representations with Structural and Temporal Bias Correction](https://mlsanity.com/paper/2508.15378/) (2025)
- [Statistical Feature Augmentation for Anomaly Detection in Dynamic Graphs](https://mlsanity.com/paper/2609.02965/) (2026)

## Abstract (arXiv)

Anomalies in online social networks can signify irregular, and often illegal behaviour. Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators, and online fraudsters. In this paper we survey existing computational techniques for detecting anomalies in online social networks. We characterise anomalies as being either static or dynamic, and as being labelled or unlabelled, and survey methods for detecting these different types of anomalies. We suggest that the detection of anomalies in online social networks is composed of two sub-processes; the selection and calculation of network features, and the classification of observations from this feature space. In addition, this paper provides an overview of the types of problems that anomaly detection can address and identifies key areas of future research.