# Semi-supervised Anomaly Detection on Attributed Graphs

arXiv: https://arxiv.org/abs/2002.12011 · HTML: https://mlsanity.com/paper/2002.12011/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2020.

## Built on by (most cited first, 18 of 18)

- [Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation](https://mlsanity.com/paper/2112.10063/) (2021)
- [Few-shot Network Anomaly Detection via Cross-network Meta-learning](https://mlsanity.com/paper/2102.11165/) (2021)
- [Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges](https://mlsanity.com/paper/2209.14930/) (2022)
- [Weakly Supervised Anomaly Detection: A Survey](https://mlsanity.com/paper/2302.04549/) (2023)
- [Graph-level Anomaly Detection via Hierarchical Memory Networks](https://mlsanity.com/paper/2307.00755/) (2023)
- [Sketch-Based Anomaly Detection in Streaming Graphs](https://mlsanity.com/paper/2106.04486/) (2021)
- [Graph Anomaly Detection with Unsupervised GNNs](https://mlsanity.com/paper/2210.09535/) (2022)
- [Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly Detection](https://mlsanity.com/paper/2407.02143/) (2024)
- [Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning](https://mlsanity.com/paper/2309.06034/) (2023)
- [Towards Cross-domain Few-shot Graph Anomaly Detection](https://mlsanity.com/paper/2410.08629/) (2024)
- [Graph Evidential Learning for Anomaly Detection](https://mlsanity.com/paper/2506.00594/) (2025)
- [PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization](https://mlsanity.com/paper/2401.10547/) (2024)
- [Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs](https://mlsanity.com/paper/2501.15035/) (2025)
- [AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies](https://mlsanity.com/paper/2305.18798/) (2023)
- [Label-based Graph Augmentation with Metapath for Graph Anomaly Detection](https://mlsanity.com/paper/2308.10918/) (2023)
- [Toward Reasoning on the Boundary: A Mixup-based Approach for Graph Anomaly Detection](https://mlsanity.com/paper/2410.20310/) (2024)
- [Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning](https://mlsanity.com/paper/2503.12037/) (2025)
- [A Survey on Graph Neural Networks for Fraud Detection in Ride Hailing Platforms](https://mlsanity.com/paper/2512.23777/) (2025)

## Abstract (arXiv)

We propose a simple yet effective method for detecting anomalous instances on an attribute graph with label information of a small number of instances. Although with standard anomaly detection methods it is usually assumed that instances are independent and identically distributed, in many real-world applications, instances are often explicitly connected with each other, resulting in so-called attributed graphs. The proposed method embeds nodes (instances) on the attributed graph in the latent space by taking into account their attributes as well as the graph structure based on graph convolutional networks (GCNs). To learn node embeddings specialized for anomaly detection, in which there is a class imbalance due to the rarity of anomalies, the parameters of a GCN are trained to minimize the volume of a hypersphere that encloses the node embeddings of normal instances while embedding anomalous ones outside the hypersphere. This enables us to detect anomalies by simply calculating the distances between the node embeddings and hypersphere center. The proposed method can effectively propagate label information on a small amount of nodes to unlabeled ones by taking into account the node's attributes, graph structure, and class imbalance. In experiments with five real-world attributed graph datasets, we demonstrate that the proposed method achieves better performance than various existing anomaly detection methods.