ANOGAT-Sparse-TL: A hybrid framework combining sparsification and graph attention for anomaly detection in attributed networks using the optimized loss function incorporating the Twersky loss for improved robustness

In recent years, the identification of abnormalities in attributed networks has become essential for applications including social media analysis, cybersecurity, and financial fraud detection. Unsupervised graph anomaly detection techniques seek to recognize infrequent and anomalous patterns in graph-structured data without the necessity of labelled instances. Conventional methods employing Graph Neural Networks (GNNs) frequently encounter difficulties, especially due to the transmission of noisy edges and the intrinsic intricacy of node interrelations. To overcome these restrictions, we introduce ANOGAT-Sparse-TL, an innovative hybrid framework that integrates graph sparsification and Graph Attention Networks (GAT) with autoencoder-based reconstruction for anomaly detection in attributed networks. The sparsification procedure removes extraneous edges and highlights significant node connections, thereby enhancing computational efficiency and improving anomaly detection efficacy. By including GAT, our model carefully allocates significance to pertinent neighboring nodes, yielding enhanced node embeddings. The autoencoder subsequently reconstructs these embeddings to detect abnormalities via reconstruction errors. Incorporating Tversky Loss in the reconstruction process further improves the robustness of the model by effectively addressing the imbalance between normal and anomalous data, prioritizing the detection of rare anomalies. This optimized loss function allows ANOGAT-Sparse-TL to focus on hard-to-reconstruct instances, which are typically indicative of anomalies, and reduces the impact of noisy data on the model's performance. ANOGAT-Sparse-TL effectively integrates attribute-based and structural anomalies, yielding comprehensive anomaly ratings. Comprehensive studies on the four real-world datasets indicate that our strategy surpasses current state-of-the-art methodologies, with enhanced performance. Moreover, the scalability of our methodology guarantees its relevance to extensive real-world networks, rendering it an adaptable option for diverse graph anomaly detection activities. ANOGAT-Sparse-TL, despite its complexity, maintains computational efficiency and provides substantial improvements in anomaly detection inside attributed networks. Future research may concentrate on enhancing interpretability and broadening generalizability to various network architectures.