Towards Multiple Missing Values-resistant Unsupervised Graph Anomaly Detection

• URL: http://arxiv.org/abs/2511.09917v1
• Date: Fri, 14 Nov 2025 01:18:37 GMT
• Title: Towards Multiple Missing Values-resistant Unsupervised Graph Anomaly Detection
• Authors: Jiazhen Chen, Xiuqin Liang, Sichao Fu, Zheng Ma, Weihua Ou,
• Abstract summary: We propose M$2$V-UGAD, a multiple missing values-resistant unsupervised GAD framework on incomplete graphs.<n>M$2$V-UGAD consistently outperforms existing unsupervised GAD methods across varying missing rates.
• Score: 10.061847825452677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised graph anomaly detection (GAD) has received increasing attention in recent years, which aims to identify data anomalous patterns utilizing only unlabeled node information from graph-structured data. However, prevailing unsupervised GAD methods typically presuppose complete node attributes and structure information, a condition hardly satisfied in real-world scenarios owing to privacy, collection errors or dynamic node arrivals. Existing standard imputation schemes risk "repairing" rare anomalous nodes so that they appear normal, thereby introducing imputation bias into the detection process. In addition, when both node attributes and edges are missing simultaneously, estimation errors in one view can contaminate the other, causing cross-view interference that further undermines the detection performance. To overcome these challenges, we propose M$^2$V-UGAD, a multiple missing values-resistant unsupervised GAD framework on incomplete graphs. Specifically, a dual-pathway encoder is first proposed to independently reconstruct missing node attributes and graph structure, thereby preventing errors in one view from propagating to the other. The two pathways are then fused and regularized in a joint latent space so that normals occupy a compact inner manifold while anomalies reside on an outer shell. Lastly, to mitigate imputation bias, we sample latent codes just outside the normal region and decode them into realistic node features and subgraphs, providing hard negative examples that sharpen the decision boundary. Experiments on seven public benchmarks demonstrate that M$^2$V-UGAD consistently outperforms existing unsupervised GAD methods across varying missing rates.

Related papers

• Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time [60.341117019125214]
  We propose a lightweight and plug-and-play Test-time adaptation framework for correcting Unseen Normal pattErns in graph anomaly detection (GAD)<n>To address semantic confusion, a graph aligner is employed to align the shifted data to the original one at the graph attribute level.<n>Extensive experiments on 10 real-world datasets demonstrate that TUNE significantly enhances the generalizability of pre-trained GAD models to both synthetic and real unseen normal patterns.
  arXiv  Detail & Related papers  (2025-11-10T12:10:05Z)
• UMGAD: Unsupervised Multiplex Graph Anomaly Detection [40.17829938834783]
  We propose a novel Unsupervised Multiplex Graph Anomaly Detection method, named UMGAD.<n>We first learn multi-relational correlations among nodes in multiplex heterogeneous graphs.<n>Then, to further extract abnormal information, we generate attribute-level and subgraph-level augmented-view graphs.
  arXiv  Detail & Related papers  (2024-11-19T15:15:45Z)
• ARC: A Generalist Graph Anomaly Detector with In-Context Learning [62.202323209244]
  ARC is a generalist GAD approach that enables a one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly.<n> equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset.<n>Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance, efficiency, and generalizability of ARC.
  arXiv  Detail & Related papers  (2024-05-27T02:42:33Z)
• Generative Semi-supervised Graph Anomaly Detection [42.02691404704764]
  This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal.<n>We propose a novel Generative GAD approach (namely GGAD) for the semi-supervised scenario to better exploit the normal nodes.<n>GGAD is designed to leverage two important priors about the anomaly nodes -- asymmetric local affinity and egocentric closeness.
  arXiv  Detail & Related papers  (2024-02-19T06:55:50Z)
• Alleviating Structural Distribution Shift in Graph Anomaly Detection [70.1022676681496]
  Graph anomaly detection (GAD) is a challenging binary classification problem. Gallon neural networks (GNNs) benefit the classification of normals from aggregating homophilous neighbors. We propose a framework to mitigate the effect of heterophilous neighbors and make them invariant.
  arXiv  Detail & Related papers  (2024-01-25T13:07:34Z)
• ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection [84.0718034981805]
  We introduce a novel framework called Anomaly-Denoised Autoencoders for Graph Anomaly Detection (ADA-GAD) In the first stage, we design a learning-free anomaly-denoised augmentation method to generate graphs with reduced anomaly levels. In the next stage, the decoders are retrained for detection on the original graph.
  arXiv  Detail & Related papers  (2023-12-22T09:02:01Z)
• Open-Set Graph Anomaly Detection via Normal Structure Regularisation [30.638274744518682]
  Open-set Graph Anomaly Detection (GAD) aims to train a detection model using a small number of normal and anomaly nodes.<n>Current supervised GAD methods tend to over-emphasise fitting the seen anomalies, leading to many errors of detecting the unseen anomalies as normal nodes.<n>We propose a novel open-set GAD approach, namely normal structure regularisation (NSReg), to achieve generalised detection ability to unseen anomalies.
  arXiv  Detail & Related papers  (2023-11-12T13:25:28Z)
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection [50.26074811655596]
  We propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE) By swapping the context embeddings between nodes and edges, we enable the mutual detection of node and edge anomalies. BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs.
  arXiv  Detail & Related papers  (2023-07-28T00:44:57Z)
• GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction [34.82023223790515]
  Graph Auto-Encoders (GAEs) encode graph data into node representations and identify anomalies by assessing the reconstruction quality of the graphs based on these representations.<n>We propose GAD-NR, a new variant of GAE that incorporates neighborhood reconstruction for graph anomaly detection.<n>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.
  arXiv  Detail & Related papers  (2023-06-02T23:23:34Z)
• Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment [22.769474986808113]
  Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph. We introduce a novel domain adaptation approach, namely Anomaly-aware Contrastive alignmenT (ACT) for GAD. ACT achieves substantially improved detection performance over 10 state-of-the-art GAD methods.
  arXiv  Detail & Related papers  (2022-12-02T11:21:48Z)
• Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation [61.39364567221311]
  Graph-level anomaly detection (GAD) describes the problem of detecting graphs that are abnormal in their structure and/or the features of their nodes. One of the challenges in GAD is to devise graph representations that enable the detection of both locally- and globally-anomalous graphs. We introduce a novel deep anomaly detection approach for GAD that learns rich global and local normal pattern information by joint random distillation of graph and node representations.
  arXiv  Detail & Related papers  (2021-12-19T05:04:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.