Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective

• URL: http://arxiv.org/abs/2505.18002v1
• Date: Fri, 23 May 2025 15:05:56 GMT
• Title: Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective
• Authors: Di Jin, Jingyi Cao, Xiaobao Wang, Bingdao Feng, Dongxiao He, Longbiao Wang, Jianwu Dang,
• Abstract summary: Graph anomaly detection aims to identify unusual patterns in graph-based data, with wide applications in fields such as web security and financial fraud detection.<n>Existing methods rely on contrastive learning, assuming that a lower similarity between a node and its local subgraph indicates abnormality.<n>The presence of interfering edges invalidates this assumption, since it introduces disruptive noise that compromises the contrastive learning process.<n>We propose a Clean-View Enhanced Graph Anomaly Detection framework (CVGAD), which includes a multi-scale anomaly awareness module to identify key sources of interference in the contrastive learning process.
• Score: 54.605073936695575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph anomaly detection aims to identify unusual patterns in graph-based data, with wide applications in fields such as web security and financial fraud detection. Existing methods typically rely on contrastive learning, assuming that a lower similarity between a node and its local subgraph indicates abnormality. However, these approaches overlook a crucial limitation: the presence of interfering edges invalidates this assumption, since it introduces disruptive noise that compromises the contrastive learning process. Consequently, this limitation impairs the ability to effectively learn meaningful representations of normal patterns, leading to suboptimal detection performance. To address this issue, we propose a Clean-View Enhanced Graph Anomaly Detection framework (CVGAD), which includes a multi-scale anomaly awareness module to identify key sources of interference in the contrastive learning process. Moreover, to mitigate bias from the one-step edge removal process, we introduce a novel progressive purification module. This module incrementally refines the graph by iteratively identifying and removing interfering edges, thereby enhancing model performance. Extensive experiments on five benchmark datasets validate the effectiveness of our approach.

Related papers

• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Cluster Aware Graph Anomaly Detection [32.791460110557104]
  We propose a cluster aware multi-view graph anomaly detection method, called CARE.<n>Our approach captures both local and global node affinities by augmenting the graph's adjacency matrix with the pseudo-label.<n>We show that the proposed similarity-guided loss is a variant of contrastive learning loss.
  arXiv  Detail & Related papers  (2024-09-15T15:41:59Z)
• Regularized Contrastive Partial Multi-view Outlier Detection [76.77036536484114]
  We propose a novel method named Regularized Contrastive Partial Multi-view Outlier Detection (RCPMOD) In this framework, we utilize contrastive learning to learn view-consistent information and distinguish outliers by the degree of consistency. Experimental results on four benchmark datasets demonstrate that our proposed approach could outperform state-of-the-art competitors.
  arXiv  Detail & Related papers  (2024-08-02T14:34:27Z)
• PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization [24.915797951829443]
  PhoGAD is a graph-based anomaly detection framework. It exploits persistent homology optimization to clarify behavioral boundaries. Experiments on intrusion, traffic, and spam datasets verify that PhoGAD has surpassed the performance of state-of-the-art (SOTA) frameworks in detection efficacy.
  arXiv  Detail & Related papers  (2024-01-19T08:13:10Z)
• GRAM: An Interpretable Approach for Graph Anomaly Detection using Gradient Attention Maps [26.011499804523808]
  We propose a novel approach to graph anomaly detection that leverages the power of interpretability to enhance performance. Our method extracts an attention map derived from gradients of graph neural networks, which serves as a basis for scoring anomalies. We extensively evaluate our approach against state-of-the-art graph anomaly detection techniques on real-world graph classification and wireless network datasets.
  arXiv  Detail & Related papers  (2023-11-10T16:14:21Z)
• 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)
• The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection [17.254383007779616]
  We argue that the performance degradation is mainly attributed to the inconsistency between topology and attribute. We propose a simple and effective method that uses the attention mechanism to adaptively fuse two views. Our model can significantly outperform stateof-the-art baselines on real-world fraud detection datasets.
  arXiv  Detail & Related papers  (2022-10-22T08:21:49Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• Bayesian Graph Contrastive Learning [55.36652660268726]
  We propose a novel perspective of graph contrastive learning methods showing random augmentations leads to encoders. Our proposed method represents each node by a distribution in the latent space in contrast to existing techniques which embed each node to a deterministic vector. We show a considerable improvement in performance compared to existing state-of-the-art methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-12-15T01:45:32Z)
• Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection [14.631674952942207]
  We propose a novel method, Self-Supervised Learning for Graph Anomaly Detection (SL-GAD) Our method constructs different contextual subgraphs based on a target node and employs two modules, generative attribute regression and multi-view contrastive learning for anomaly detection. We conduct extensive experiments on six benchmark datasets and the results demonstrate that our method outperforms state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2021-08-23T02:15:21Z)
• Contrastive Predictive Coding for Anomaly Detection [0.0]
  Contrastive Predictive Coding model (arXiv:1807.03748) used for anomaly detection and segmentation. We show that its patch-wise contrastive loss can directly be interpreted as an anomaly score. Model achieves promising results for both anomaly detection and segmentation on the MVTec-AD dataset.
  arXiv  Detail & Related papers  (2021-07-16T11:04:35Z)

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.