An Unsupervised Anomaly Detection Model for Weighted Heterogeneous Graph

Nowadays, whereas the use of social networks and computer networks is increasing, the amount of associated complex data with graph structure and their applications such as classification, clustering, link prediction, and recommender systems has risen significantly. Because of security problems and societal concerns, anomaly detection is becoming a vital problem in most fields. Applications that use a heterogeneous graph are confronted with many issues, such as different kinds of neighbors, different feature types, and differences in type and number of links. Thus in this research work, we employ the HetGNN model with some changes in loss functions and parameters for heterogeneous graph embedding to capture the whole graph features (structure and content) for anomaly detection, then pass it to a VAE to discover anomalous nodes based on reconstruction error. Our experiments on the AMiner data set with many base-lines illustrate that our model outperforms state-of-the-arts methods in heterogeneous graphs while considering all types of attributes.