Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach

• URL: http://arxiv.org/abs/2605.25429v1
• Date: Mon, 25 May 2026 05:12:26 GMT
• Title: Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach
• Abstract summary: Generalist graph anomaly detection (GAD) aims to detect anomalies on unseen graphs without graph-specific retraining.<n>Existing approaches primarily focus on aligning heterogeneous data across domains, which ignores different feature semantics.<n>We propose Fingerprint-based generalist GAD approach (ReFi-GAD)<n>We show that ReFi-GAD significantly outperforms state-of-the-art methods in experiments on 14 datasets.
• Score: 60.199985286210904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generalist graph anomaly detection (GAD) aims to detect anomalies on unseen graphs without graph-specific retraining. Nevertheless, existing approaches primarily focus on aligning heterogeneous features across different data domains via PCA-based projection, which harmonizes feature dimensions ignores feature semantics. As a result, GAD models fail to learn transferable semantic knowledge, and even exhibit negative transfer on unseen graphs. To address this issue, we propose a Relational Fingerprint-based generalist GAD approach (ReFi-GAD for short), aligning heterogeneous raw features with a universal and semantics-aware Relational Fingerprint (ReFi) that encodes anomaly-indicative cues from both contextual and structural perspectives. Building on ReFi, we design a fingerprint-grounded generalist GAD model, which combines a transformer-based encoder to capture domain-invariant knowledge with an SNR-guided refinement module for domain-specific adaptation. Extensive experiments on 14 datasets demonstrate that ReFi-GAD significantly outperforms state-of-the-art methods.

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