DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector

• URL: http://arxiv.org/abs/2410.06549v1
• Date: Wed, 9 Oct 2024 05:02:56 GMT
• Title: DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector
• Authors: Jinghan Li, Yuan Gao, Jinda Lu, Junfeng Fang, Congcong Wen, Hui Lin, Xiang Wang,
• Abstract summary: We present a Diffusion-based Graph Anomaly Detector (DiffGAD) At the heart of DiffGAD is a novel latent space learning paradigm, meticulously designed to enhance its proficiency by guiding it with discriminative content. Our comprehensive evaluation of DiffGAD, conducted on six real-world and large-scale datasets, demonstrated its exceptional performance.
• Score: 17.191834562399293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Anomaly Detection (GAD) is crucial for identifying abnormal entities within networks, garnering significant attention across various fields. Traditional unsupervised methods, which decode encoded latent representations of unlabeled data with a reconstruction focus, often fail to capture critical discriminative content, leading to suboptimal anomaly detection. To address these challenges, we present a Diffusion-based Graph Anomaly Detector (DiffGAD). At the heart of DiffGAD is a novel latent space learning paradigm, meticulously designed to enhance its proficiency by guiding it with discriminative content. This innovative approach leverages diffusion sampling to infuse the latent space with discriminative content and introduces a content-preservation mechanism that retains valuable information across different scales, significantly improving its adeptness at identifying anomalies with limited time and space complexity. Our comprehensive evaluation of DiffGAD, conducted on six real-world and large-scale datasets with various metrics, demonstrated its exceptional performance.

Related papers

• A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection [60.09453163562244]
  We propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD. In the estimation module, we design a novel label-free heterophily metric called HALO, which captures the critical graph properties for GFD. In the alignment-based fraud detection module, we develop a joint-GNN architecture with ranking loss and asymmetric alignment loss.
  arXiv  Detail & Related papers  (2025-02-18T22:07:36Z)
• Out-of-Distribution Detection on Graphs: A Survey [58.47395497985277]
  Graph out-of-distribution (GOOD) detection focuses on identifying graph data that deviates from the distribution seen during training. We categorize existing methods into four types: enhancement-based, reconstruction-based, information propagation-based, and classification-based approaches. We discuss practical applications and theoretical foundations, highlighting the unique challenges posed by graph data.
  arXiv  Detail & Related papers  (2025-02-12T04:07:12Z)
• Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs [5.415950005432774]
  We propose a novel GAD framework (EL$2$-DGAD) to tackle anomaly detection problem in dynamic graphs with extremely limited labels. Specifically, a transformer-based graph encoder model is designed to more effectively preserve evolving graph structures beyond the local neighborhood.
  arXiv  Detail & Related papers  (2025-01-25T02:35:48Z)
• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• 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. equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset. 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)
• FGAD: Self-boosted Knowledge Distillation for An Effective Federated Graph Anomaly Detection Framework [33.62637380192881]
  Graph anomaly detection (GAD) aims to identify anomalous graphs that significantly deviate from other ones. Existing GAD methods usually execute with centralized training, which may lead to privacy leakage risk in some sensitive cases. We propose an effective federated graph anomaly detection framework (FGAD) to tackle these challenges.
  arXiv  Detail & Related papers  (2024-02-20T07:03:59Z)
• Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection [15.757864894708364]
  Graph anomaly detection plays a crucial role in identifying exceptional instances in graph data that deviate significantly from the majority. We propose a novel few-shot Graph Anomaly Detection model called FMGAD. We show that FMGAD can achieve better performance than other state-of-the-art methods, regardless of artificially injected anomalies or domain-organic anomalies.
  arXiv  Detail & Related papers  (2023-11-17T07:49:20Z)
• Enhancing Unsupervised Anomaly Detection with Score-Guided Network [13.127091975959358]
  Anomaly detection plays a crucial role in various real-world applications, including healthcare and finance systems. We propose a novel scoring network with a score-guided regularization to learn and enlarge the anomaly score disparities between normal and abnormal data. We next propose a score-guided autoencoder (SG-AE), incorporating the scoring network into an autoencoder framework for anomaly detection.
  arXiv  Detail & Related papers  (2021-09-10T06:14:53Z)
• Graph Convolutional Networks for traffic anomaly [4.172516437934823]
  Event detection has been an important task in transportation, whose task is to detect points in time when large events disrupts a large portion of the urban traffic network. To fully capture the spatial and temporal traffic patterns remains a challenge, yet serves a crucial role for effective anomaly detection. We formulate the problem in a novel way, as detecting anomalies in a set of directed weighted graphs representing the traffic conditions at each time interval.
  arXiv  Detail & Related papers  (2020-12-25T22:36:22Z)
• ESAD: End-to-end Deep Semi-supervised Anomaly Detection [85.81138474858197]
  We propose a new objective function that measures the KL-divergence between normal and anomalous data. The proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-09T08:16:35Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

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.