Dual contrastive learning-based reconstruction for anomaly detection in attributed networks

Anomaly detection in attributed networks is critical for identifying threats such as financial fraud and intrusions across social, e-commerce, and cyber-physical domains. Existing graph-based methods face two limitations: (i) embedding-based approaches obscure fine-grained structural and attribute patterns, and (ii) reconstruction-based methods neglect cross-view discrepancies during training, leaving cross-view discrepancies underutilized. To address these gaps, we propose Dual Contrastive Learning-based Reconstruction (DCOR), a dual autoencoder with a shared Graph neural network (GNN) encoder that contrasts reconstructions (not embeddings) between original and augmented graph views. Instead of contrasting embeddings, DCOR reconstructs both adjacency and attributes for the original graph and for an augmented view, then contrasts the reconstructions across views. This preserves fine-grained, view-specific information and improves the fidelity of both structure and attributes. Across six benchmarks (Enron, Amazon, Facebook, Flickr, ACM, and Reddit), DCOR achieves the best Area Under the Receiver Operating Characteristic curve (AUROC) on six datasets. In comparison with the best-performing non-DCOR baseline across datasets, DCOR improves AUROC by 11.3% on average, with a maximum gain of 21.3% on Enron. On Amazon, ablating the reconstruction-level contrast (RLC) reduces AUROC by 25.5% relative to the model, underscoring the necessity of reconstruction-level contrastive learning. Code and datasets are publicly available at https://github.com/Hossein1998/DCOR-Graph-Anomaly-Detection.git.