An unsupervised deep learning ensemble model for anomaly detection in static attributed social networks

Abstract

Due to its importance in several applications, including fraud and spammer detection, anomaly detection has emerged as a key challenge in social network analysis in recent years. By including both graph and node properties, aberrant nodes in static attributed networks can be identified with more precision. Anomaly detection algorithms based on deep learning have attracted a lot of attention because the classic detection methods have significant limitations in the face of the massive growth in data volume and dimensionality. Most studies in this area focus on developing supervised techniques for spotting outliers. However, supervised algorithms for anomaly detection cannot be properly used because there is insufficient labeled data in real-world problems. The fact that the anomalies themselves, as well as the ground truth and the class labels, are unknown, presents a substantial difficulty for unsupervised anomaly detection. Several researchers have developed unsupervised learning strategies for using unlabeled data to train detection models. However, because of the diversity in anomaly types, the inability of any one model to identify all possible anomalies is a major limitation of all such models. To better detect anomalies in social networks, we created an ensemble model that combines models utilizing autoencoders (AEs), variational autoencoders (VAEs), and generative adversarial networks (GANs). During the inference (detection) phase, we use a novel approach for assigning the weights to various models used in our ensemble model. Extensive tests on two popular datasets were done to demonstrate the usefulness of the proposed ensemble.: the BlogCatalog, and Flickr. Experiments have shown that our suggested model vastly outperforms the state-of-the-art approaches utilized individually in the ensemble.