Subgraph Anomaly Detection in Social Networks using Clustering-Based Deep Autoencoders

Abstract

Social networks are becoming more prevalent all across the globe. With all of its advantages, criminality and fraudulent conduct in this medium are on the rise. As a result, there is an urgent need to detect abnormalities in these networks before they do substantial harm. Traditional Non-Deep Learning (NDL) approaches fails to perform effectively when the size and scope of real-world social networks increase. As a result, DL techniques for anomaly detection in social networks are required. Several studies have been conducted using DL on node and edge anomaly detection. However, in the current scenario, subgraph anomaly detection utilizing Deep Learning (DL) is still in its nascent stages. This paper proposes a method called Clustering-based Deep Autoencoders (CDA) to detect subgraph anomalies in static attributed social networks. It converts the input graph into node embeddings using an encoder, clusters these nodes into communities or subgraphs, and then finds anomalies among these subgraph embeddings. The model is tested on seven open-access social network datasets, and the findings indicate that the proposed model detects the most anomalies. In the future, it is also recommended that the present experiment be aimed at dynamic social networks.