DualGAD: A Generalist Graph Anomaly Detection Method via Dual-Encoder Architecture

Due to the capability of graph structures to model complex relationships, graph anomaly detection has significant application value in various domains, including financial fraud detection, network security, and fake account identification. Traditional graph anomaly detection methods follow a specialized paradigm of “one dataset, one model”, which requires retraining or fine-tuning models for each new domain. This approach faces critical challenges in practical applications, namely high deployment costs and limited generalization capability. To address this problem, generalist graph anomaly detection aims to achieve the goal of “train once, apply across domains”. However, existing generalist methods primarily rely on graph neural networks to implicitly learn structural information, where the learned structural representations are tightly coupled with specific topology distributions, resulting in limited structural stability under domain shifts. To address this limitation, we propose DualGAD, a generalist graph anomaly detection method via a dual-encoder architecture. In particular, DualGAD introduces explicit structural modeling that characterizes the relative topological deviation of nodes with respect to the overall graph structure, thereby enhancing structural invariance across heterogeneous domains. This method separately models node attribute information and explicit graph structural information via an attribute feature encoder and an explicit structural feature encoder, and adopts an “attribute-dominant, structure-complementary” fusion strategy to achieve collaborative modeling. Experiments on eight real datasets demonstrate that DualGAD achieves an average improvement of 3.12% in AUROC compared to the strongest baseline methods, exhibiting significant cross-domain generalization capability.