Graph Anomaly Detection with Noisy Labels by Reinforcement Learning

• URL: http://arxiv.org/abs/2407.05934v1
• Date: Mon, 8 Jul 2024 13:41:21 GMT
• Title: Graph Anomaly Detection with Noisy Labels by Reinforcement Learning
• Authors: Zhu Wang, Shuang Zhou, Junnan Dong, Chang Yang, Xiao Huang, Shengjie Zhao,
• Abstract summary: We propose a novel framework REGAD, i.e., REinforced Graph Anomaly Detector. Specifically, we aim to maximize the performance improvement (AUC) of a base detector by cutting noisy edges approximated through the nodes with high-confidence labels.
• Score: 13.135788402192215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph anomaly detection (GAD) has been widely applied in many areas, e.g., fraud detection in finance and robot accounts in social networks. Existing methods are dedicated to identifying the outlier nodes that deviate from normal ones. While they heavily rely on high-quality annotation, which is hard to obtain in real-world scenarios, this could lead to severely degraded performance based on noisy labels. Thus, we are motivated to cut the edges of suspicious nodes to alleviate the impact of noise. However, it remains difficult to precisely identify the nodes with noisy labels. Moreover, it is hard to quantitatively evaluate the regret of cutting the edges, which may have either positive or negative influences. To this end, we propose a novel framework REGAD, i.e., REinforced Graph Anomaly Detector. Specifically, we aim to maximize the performance improvement (AUC) of a base detector by cutting noisy edges approximated through the nodes with high-confidence labels. (i) We design a tailored action and search space to train a policy network to carefully prune edges step by step, where only a few suspicious edges are prioritized in each step. (ii) We design a policy-in-the-loop mechanism to iteratively optimize the policy based on the feedback from base detector. The overall performance is evaluated by the cumulative rewards. Extensive experiments are conducted on three datasets under different anomaly ratios. The results indicate the superior performance of our proposed REGAD.

Related papers

• Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement [33.565252991113766]
  Graph anomaly detection (GAD) is increasingly crucial in various applications, ranging from financial fraud detection to fake news detection. Current GAD methods largely overlook the fairness problem, which might result in discriminatory decisions skewed toward certain demographic groups. We devise a novel DisEntangle-based FairnEss-aware aNomaly Detection framework on the attributed graph, named DEFEND. Our empirical evaluations on real-world datasets reveal that DEFEND performs effectively in GAD and significantly enhances fairness compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-06-03T04:48:45Z)
• 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)
• Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection [22.322241872706314]
  Unsupervised graph anomaly detection is crucial for various practical applications. Recent advancements have utilized Graph Neural Networks (GNNs) to learn high-quality node representations for anomaly detection. We propose a novel method that incorporates Reinforcement neighborhood selection for unsupervised graph ANomaly Detection (RAND)
  arXiv  Detail & Related papers  (2023-12-09T10:39:45Z)
• 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)
• Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
  We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection. A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes. Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
  arXiv  Detail & Related papers  (2023-07-11T07:48:39Z)
• EDoG: Adversarial Edge Detection For Graph Neural Networks [17.969573886307906]
  Graph Neural Networks (GNNs) have been widely applied to different tasks such as bioinformatics, drug design, and social networks. Recent studies have shown that GNNs are vulnerable to adversarial attacks which aim to mislead the node or subgraph classification prediction by adding subtle perturbations. We propose a general adversarial edge detection pipeline EDoG without requiring knowledge of the attack strategies based on graph generation.
  arXiv  Detail & Related papers  (2022-12-27T20:42:36Z)
• 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)
• Deep Fraud Detection on Non-attributed Graph [61.636677596161235]
  Graph Neural Networks (GNNs) have shown solid performance on fraud detection. labeled data is scarce in large-scale industrial problems, especially for fraud detection. We propose a novel graph pre-training strategy to leverage more unlabeled data.
  arXiv  Detail & Related papers  (2021-10-04T03:42:09Z)
• Improving Face Recognition by Clustering Unlabeled Faces in the Wild [77.48677160252198]
  We propose a novel identity separation method based on extreme value theory. It greatly reduces the problems caused by overlapping-identity label noise. Experiments on both controlled and real settings demonstrate our method's consistent improvements.
  arXiv  Detail & Related papers  (2020-07-14T12:26:50Z)
• Solving Missing-Annotation Object Detection with Background Recalibration Loss [49.42997894751021]
  This paper focuses on a novel and challenging detection scenario: A majority of true objects/instances is unlabeled in the datasets. Previous art has proposed to use soft sampling to re-weight the gradients of RoIs based on the overlaps with positive instances, while their method is mainly based on the two-stage detector. In this paper, we introduce a superior solution called Background Recalibration Loss (BRL) that can automatically re-calibrate the loss signals according to the pre-defined IoU threshold and input image.
  arXiv  Detail & Related papers  (2020-02-12T23:11:46Z)

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.