Anomaly detection in graph databases using graph neural networks: Identifying unusual patterns in graphs

Anomaly detection in graph-structured data is a critical task in various applications, including social networks, fraud detection, and educational platforms. This paper introduces a novel hybrid architecture that leverages Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and Graph Autoencoders (GAEs) to detect anomalies across nodes, edges, and subgraphs. The proposed model combines the strengths of GCNs for extracting local structural features, GATs for adaptive attention-based neighborhood aggregation, and GAEs for unsupervised graph reconstruction. By integrating these components, our approach generates robust embeddings that are used to calculate anomaly scores based on reconstruction errors, density estimation, and embedding distances. These scores are then aggregated using a weighted hybrid function, enabling adaptive and flexible anomaly scoring. Experimental results on benchmark datasets demonstrate that the hybrid model significantly improves the detection of anomalous nodes, edges, and subgraphs compared to existing methods. This work provides a scalable and effective framework for anomaly detection in graphs, offering insights into the interpretability and adaptability of GNN-based anomaly scoring functions.