Deep Detection of Anomalies in Static Attributed Graph

Abstract

While online social media is one of the greatest innovations of modern man, it often gets used to perform a barrage of malicious activities which can be anomalous in nature. The area of anomaly detection deals with this challenging task. In this paper, we methodically investigate anomaly detection for the modern content driven attributed graphs. Since labeled graph data is not available for scientific research, we work with a synthetically generated dataset with an unsupervised learning approach to prove that both attribute as well as structure should be considered. We also investigate whether deep learning in this context brings an additional advantage in anomaly detection. We extend the recent work in this area, with an innovative combination of attributed graph embedding with graph convolution technique.