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

## TL;DR

This paper introduces DCOR, a new method for detecting anomalies in networks by improving reconstruction through dual contrastive learning.

## Contribution

DCOR introduces reconstruction-level contrastive learning to better preserve structural and attribute patterns in attributed networks.

## Key findings

- DCOR achieves the best AUROC on six benchmark datasets for anomaly detection.
- Reconstruction-level contrast improves performance by up to 21.3% on the Enron dataset.
- Ablation studies show that removing reconstruction-level contrast reduces performance by 25.5% on Amazon.

## Abstract

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.