A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection

• URL: http://arxiv.org/abs/2502.13308v1
• Date: Tue, 18 Feb 2025 22:07:36 GMT
• Title: A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection
• Authors: Junjun Pan, Yixin Liu, Xin Zheng, Yizhen Zheng, Alan Wee-Chung Liew, Fuyi Li, Shirui Pan,
• Abstract summary: 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.
• Score: 60.09453163562244
• License:
• Abstract: Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic connections between fraudsters and users can greatly impact detection performance, since fraudsters tend to camouflage themselves by building more connections to benign users. Despite the promising performance of supervised GFD methods, the reliance on labels limits their applications to unsupervised scenarios; Additionally, accurately capturing complex and diverse heterophily patterns without labels poses a further challenge. To fill the gap, we propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD, which contains two essential components: a heterophily estimation module and an alignment-based fraud detection module. In the heterophily estimation module, we design a novel label-free heterophily metric called HALO, which captures the critical graph properties for GFD, enabling its outstanding ability to estimate heterophily from node attributes. In the alignment-based fraud detection module, we develop a joint MLP-GNN architecture with ranking loss and asymmetric alignment loss. The ranking loss aligns the predicted fraud score with the relative order of HALO, providing an extra robustness guarantee by comparing heterophily among non-adjacent nodes. Moreover, the asymmetric alignment loss effectively utilizes structural information while alleviating the feature-smooth effects of GNNs.Extensive experiments on 6 datasets demonstrate that HUGE significantly outperforms competitors, showcasing its effectiveness and robustness. The source code of HUGE is at https://github.com/CampanulaBells/HUGE-GAD.

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)
• Revisiting Graph-Based Fraud Detection in Sight of Heterophily and Spectrum [26.62679288320554]
  Graph-based fraud detection (GFD) can be regarded as a challenging semi-supervised node binary classification task. This paper proposes a semi-supervised GNN-based fraud detector SEC-GFD. The comprehensive experimental results on four real-world fraud detection datasets denote that SEC-GFD outperforms other competitive graph-based fraud detectors.
  arXiv  Detail & Related papers  (2023-12-11T15:18:51Z)
• 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)
• 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)
• Resisting Graph Adversarial Attack via Cooperative Homophilous Augmentation [60.50994154879244]
  Recent studies show that Graph Neural Networks are vulnerable and easily fooled by small perturbations. In this work, we focus on the emerging but critical attack, namely, Graph Injection Attack. We propose a general defense framework CHAGNN against GIA through cooperative homophilous augmentation of graph data and model.
  arXiv  Detail & Related papers  (2022-11-15T11:44:31Z)
• 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)
• ScoreGAN: A Fraud Review Detector based on Multi Task Learning of Regulated GAN with Data Augmentation [50.779498955162644]
  We propose ScoreGAN for fraud review detection that makes use of both review text and review rating scores in the generation and detection process. Results show that the proposed framework outperformed the existing state-of-the-art framework, namely FakeGAN, in terms of AP by 7%, and 5% on the Yelp and TripAdvisor datasets.
  arXiv  Detail & Related papers  (2020-06-11T16:15:06Z)
• Alleviating the Inconsistency Problem of Applying Graph Neural Network to Fraud Detection [78.88163190021798]
  We introduce a new GNN framework, $mathsfGraphConsis$, to tackle the inconsistency problem. Empirical analysis on four datasets indicates the inconsistency problem is crucial in a fraud detection task. We also released a GNN-based fraud detection toolbox with implementations of SOTA models.
  arXiv  Detail & Related papers  (2020-05-01T21:43:58Z)

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.