# SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks

arXiv: https://arxiv.org/abs/1908.03849 · HTML: https://mlsanity.com/paper/1908.03849/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2019.

## Built on by (most cited first, 30 of 37)

- [Graph Self-Supervised Learning: A Survey](https://mlsanity.com/paper/2103.00111/) (2021)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning](https://mlsanity.com/paper/2103.00113/) (2021)
- [Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection](https://mlsanity.com/paper/2108.09896/) (2021)
- [Few-shot Network Anomaly Detection via Cross-network Meta-learning](https://mlsanity.com/paper/2102.11165/) (2021)
- [GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction](https://mlsanity.com/paper/2306.01951/) (2023)
- [Truncated Affinity Maximization: One-class Homophily Modeling for Graph Anomaly Detection](https://mlsanity.com/paper/2306.00006/) (2023)
- [Deep Graph Anomaly Detection: A Survey and New Perspectives](https://mlsanity.com/paper/2409.09957/) (2024)
- [Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/2205.04816/) (2022)
- [PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection](https://mlsanity.com/paper/2310.11676/) (2023)
- [Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges](https://mlsanity.com/paper/2209.14930/) (2022)
- [ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection](https://mlsanity.com/paper/2312.14535/) (2023)
- [A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems](https://mlsanity.com/paper/2206.04149/) (2022)
- [Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges](https://mlsanity.com/paper/2212.05532/) (2022)
- [A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions](https://mlsanity.com/paper/2308.13821/) (2023)
- [UniGAD: Unifying Multi-level Graph Anomaly Detection](https://mlsanity.com/paper/2411.06427/) (2024)
- [From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach](https://mlsanity.com/paper/2202.05525/) (2022)
- [Higher-order Structure Based Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/2406.04690/) (2024)
- [Open-Set Graph Anomaly Detection via Normal Structure Regularisation](https://mlsanity.com/paper/2311.06835/) (2023)
- [FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection](https://mlsanity.com/paper/2508.10594/) (2025)
- [Learning Disentangled Representations for Time Series](https://mlsanity.com/paper/2105.08179/) (2021)
- [Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model](https://mlsanity.com/paper/2308.01947/) (2023)
- [Subgraph Centralization: A Necessary Step for Graph Anomaly Detection](https://mlsanity.com/paper/2301.06794/) (2023)
- [Deep Dual Support Vector Data Description for Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/2109.00138/) (2021)
- [Anomaly Detection in Graph Structured Data: A Survey](https://mlsanity.com/paper/2405.06172/) (2024)
- [UMGAD: Unsupervised Multiplex Graph Anomaly Detection](https://mlsanity.com/paper/2411.12556/) (2024)
- [OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detection](https://mlsanity.com/paper/2601.19102/) (2026)
- [Hyperbolic Self-supervised Contrastive Learning Based Network Anomaly Detection](https://mlsanity.com/paper/2209.05049/) (2022)
- [Label-based Graph Augmentation with Metapath for Graph Anomaly Detection](https://mlsanity.com/paper/2308.10918/) (2023)
- [Novel Node Category Detection Under Subpopulation Shift](https://mlsanity.com/paper/2404.01216/) (2024)

## Abstract (arXiv)

Anomaly detection aims to distinguish observations that are rare and different from the majority. While most existing algorithms assume that instances are i.i.d., in many practical scenarios, links describing instance-to-instance dependencies and interactions are available. Such systems are called attributed networks. Anomaly detection in attributed networks has various applications such as monitoring suspicious accounts in social media and financial fraud in transaction networks. However, it remains a challenging task since the definition of anomaly becomes more complicated and topological structures are heterogeneous with nodal attributes. In this paper, we propose a spectral convolution and deconvolution based framework -- SpecAE, to project the attributed network into a tailored space to detect global and community anomalies. SpecAE leverages Laplacian sharpening to amplify the distances between representations of anomalies and the ones of the majority. The learned representations along with reconstruction errors are combined with a density estimation model to perform the detection. They are trained jointly as an end-to-end framework. Experiments on real-world datasets demonstrate the effectiveness of SpecAE.