# Enhanced Graph Autoencoder for Graph Anomaly Detection Using Subgraph Information
###### Abstract  
Graph anomaly detection aims at identifying rare, unusual entities in attributed networks with respect to their patterns or structures that deviate significantly from the majority within a graph. Over the years, extensive efforts in this field have been dedicated to the powerful capability of attributed networks to model real-world systems. Given the scarcity of labeled anomalies, current research primarily emphasizes model design via unsupervised learning. Graph autoencoders have been widely utilized for such purposes, leveraging the outstanding capabilities of Graph Neural Networks to model graph structured data. However, most existing graph autoencoder-based anomaly detectors do not exploit the nodes' local subgraph information, limiting their ability to comprehensively understand the network for better representation learning. Moreover, these methods place greater emphasis on the attribute reconstruction process while neglecting the structure reconstruction aspect. This paper proposes an enhanced graph autoencoder framework for graph anomaly detection tasks that incorporates a subgraph extraction and aggregation preprocessing stage to utilize the nodes' local topological information for enhanced embedding generation and to induce an additional node-subgraph view through model learning. A graph structure learning-based decoder is introduced as the structure decoder for better relationship learning. Finally, during the anomaly scoring stage, a node neighborhood selection technique is applied to enhance the detection performance. The effectiveness of the proposed framework is demonstrated through comprehensive experiments conducted on six commonly used real-world datasets.  
graph anomaly detection; graph neural networks; graph autoencoders; graph structure learning +
Footnote †: journal: Applied sciences  
representation learning and anomaly identification. However, these models utilize only the node attribute information, and addressing the limitations in capturing local and structural information to achieve better detection performance remains a challenging task [1]. The primary purpose of this study is to propose an improved graph autoencoder-based framework capable of effectively handling the challenges present in current frameworks, through the introduction of the following improvements: (1) a subgraph preprocessing stage to aggregate the nodes' local topological subgraph topological information and generate enhanced embeddings; (2) a graph structure learning module for improved graph structure reconstruction in the decoding stage; and (3) a neighborhood selection method for better anomaly scoring ability. The goal is to consistently outperform state-of-the-art graph anomaly detection models when evaluated on real-world datasets.  
Inspired by recent developments in Graph Neural Network frameworks that incorporate the use of subgraph information and multi-view learning, this paper proposes a subgraph preprocessing stage designed to extract and aggregate subgraph-level topological information, alongside the original node attributes. This enhanced attribute input is intended to strengthen the node representation learning capability of the proposed graph autoencoder-based model. Furthermore, an additional node-subgraph view is introduced to inject more structural information into the learned embeddings, thereby augmenting the model's understanding of the attributed networks. In the decoder phase, the proposed model employs a neural-based graph structure learning technique within the structure decoder to improve the reconstruction of node relationships based on the learned representations. Finally, a specially designed neighborhood selection method is introduced to provide better performance in the anomaly scoring stage after training.