# GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction

arXiv: https://arxiv.org/abs/2306.01951 · HTML: https://mlsanity.com/paper/2306.01951/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 94 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 30 of 31)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Principal Neighbourhood Aggregation for Graph Nets](https://mlsanity.com/paper/2004.05718/) (2020)
- [Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks](https://mlsanity.com/paper/1908.01207/) (2019)
- [Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters](https://mlsanity.com/paper/2008.08692/) (2020)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Rethinking Graph Neural Networks for Anomaly Detection](https://mlsanity.com/paper/2205.15508/) (2022)
- [Graph-based Anomaly Detection and Description: A Survey](https://mlsanity.com/paper/1404.4679/) (2014)
- [Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning](https://mlsanity.com/paper/2103.00113/) (2021)
- [A Semi-supervised Graph Attentive Network for Financial Fraud Detection](https://mlsanity.com/paper/2003.01171/) (2020)
- [AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks](https://mlsanity.com/paper/2002.03665/) (2020)
- [BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs](https://mlsanity.com/paper/2206.10071/) (2022)
- [GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detection](https://mlsanity.com/paper/2005.10150/) (2020)
- [BotRGCN: Twitter Bot Detection with Relational Graph Convolutional Networks](https://mlsanity.com/paper/2106.13092/) (2021)
- [SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks](https://mlsanity.com/paper/1908.03849/) (2019)
- [PyGOD: A Python Library for Graph Outlier Detection](https://mlsanity.com/paper/2204.12095/) (2022)
- [Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/2104.07917/) (2021)
- [Outlier Aware Network Embedding for Attributed Networks](https://mlsanity.com/paper/1811.07609/) (2018)
- [Graph Auto-Encoder Via Neighborhood Wasserstein Reconstruction](https://mlsanity.com/paper/2202.09025/) (2022)
- [AntiBenford Subgraphs: Unsupervised Anomaly Detection in Financial Networks](https://mlsanity.com/paper/2205.13426/) (2022)
- [A Survey on Social Media Anomaly Detection](https://mlsanity.com/paper/1601.01102/) (2016)
- [Anomaly detection in online social networks](https://mlsanity.com/paper/1608.00301/) (2016)
- [Multi-scale Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/1912.04144/) (2019)
- [Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view information](https://mlsanity.com/paper/2212.05478/) (2022)

## Built on by (most cited first, 30 of 45)

- [Deep Graph Anomaly Detection: A Survey and New Perspectives](https://mlsanity.com/paper/2409.09957/) (2024)
- [GUARDIAN: Safeguarding LLM Multi-Agent Collaborations with Temporal Graph Modeling](https://mlsanity.com/paper/2505.19234/) (2025)
- [Generative Semi-supervised Graph Anomaly Detection](https://mlsanity.com/paper/2402.11887/) (2024)
- [UniGAD: Unifying Multi-level Graph Anomaly Detection](https://mlsanity.com/paper/2411.06427/) (2024)
- [A Survey on Learning from Graphs with Heterophily: Recent Advances and Future Directions](https://mlsanity.com/paper/2401.09769/) (2024)
- [SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection](https://mlsanity.com/paper/2405.17525/) (2024)
- [DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector](https://mlsanity.com/paper/2410.06549/) (2024)
- [Revisiting Graph Contrastive Learning on Anomaly Detection: A Structural Imbalance Perspective](https://mlsanity.com/paper/2507.14677/) (2025)
- [Graph Evidential Learning for Anomaly Detection](https://mlsanity.com/paper/2506.00594/) (2025)
- [Text-Attributed Graph Anomaly Detection via Multi-Scale Cross- and Uni-Modal Contrastive Learning](https://mlsanity.com/paper/2508.00513/) (2025)
- [CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection](https://mlsanity.com/paper/2502.08605/) (2025)
- [Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection](https://mlsanity.com/paper/2512.18133/) (2025)
- [Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement](https://mlsanity.com/paper/2406.00987/) (2024)
- [Court of LLMs: Evidence-Augmented Generation via Multi-LLM Collaboration for Text-Attributed Graph Anomaly Detection](https://mlsanity.com/paper/2508.00507/) (2025)
- [CAMERA: Adapting to Semantic Camouflage in Unsupervised Text-Attributed Graph Fraud Detection](https://mlsanity.com/paper/2605.20032/) (2026)
- [Coupled-Space Attacks against Random-Walk-based Anomaly Detection](https://mlsanity.com/paper/2307.14387/) (2023)
- [Global Confidence Degree Based Graph Neural Network for Financial Fraud Detection](https://mlsanity.com/paper/2407.17333/) (2024)
- [UMGAD: Unsupervised Multiplex Graph Anomaly Detection](https://mlsanity.com/paper/2411.12556/) (2024)
- [GRASPED: Graph Anomaly Detection using Autoencoder with Spectral Encoder and Decoder (Full Version)](https://mlsanity.com/paper/2508.15633/) (2025)
- [AC2L-GAD: Active Counterfactual Contrastive Learning for Graph Anomaly Detection](https://mlsanity.com/paper/2601.21171/) (2026)
- [NeiGAD: Augmenting Graph Anomaly Detection via Spectral Neighbor Information](https://mlsanity.com/paper/2603.28300/) (2026)
- [Generalist Graph Anomaly Detection via Prototype-Based Distillation](https://mlsanity.com/paper/2605.26857/) (2026)
- [Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection](https://mlsanity.com/paper/2404.16366/) (2024)
- [High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel Approach](https://mlsanity.com/paper/2411.01817/) (2024)
- [Human-Centric Community Detection in Hybrid Metaverse Networks with Integrated AI Entities](https://mlsanity.com/paper/2502.10750/) (2025)
- [Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph Learning](https://mlsanity.com/paper/2503.12037/) (2025)
- [Reconciling Attribute and Structural Anomalies for Improved Graph Anomaly Detection](https://mlsanity.com/paper/2506.23469/) (2025)
- [Robust Anomaly Detection with Graph Neural Networks using Controllability](https://mlsanity.com/paper/2507.13954/) (2025)
- [Enhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection](https://mlsanity.com/paper/2508.10785/) (2025)
- [CRoC: Context Refactoring Contrast for Graph Anomaly Detection with Limited Supervision](https://mlsanity.com/paper/2508.12278/) (2025)

## Abstract (arXiv)

Graph Anomaly Detection (GAD) is a technique used to identify abnormal nodes within graphs, finding applications in network security, fraud detection, social media spam detection, and various other domains. A common method for GAD is Graph Auto-Encoders (GAEs), which encode graph data into node representations and identify anomalies by assessing the reconstruction quality of the graphs based on these representations. However, existing GAE models are primarily optimized for direct link reconstruction, resulting in nodes connected in the graph being clustered in the latent space. As a result, they excel at detecting cluster-type structural anomalies but struggle with more complex structural anomalies that do not conform to clusters. To address this limitation, we propose a novel solution called GAD-NR, a new variant of GAE that incorporates neighborhood reconstruction for graph anomaly detection. GAD-NR aims to reconstruct the entire neighborhood of a node, encompassing the local structure, self-attributes, and neighbor attributes, based on the corresponding node representation. By comparing the neighborhood reconstruction loss between anomalous nodes and normal nodes, GAD-NR can effectively detect any anomalies. Extensive experimentation conducted on six real-world datasets validates the effectiveness of GAD-NR, showcasing significant improvements (by up to 30% in AUC) over state-of-the-art competitors. The source code for GAD-NR is openly available. Importantly, the comparative analysis reveals that the existing methods perform well only in detecting one or two types of anomalies out of the three types studied. In contrast, GAD-NR excels at detecting all three types of anomalies across the datasets, demonstrating its comprehensive anomaly detection capabilities.