Unsupervised Community Detection Framework for Social Network Forensics

Social network forensics aims to collect and analyze social media user information and communication content. When people communicate through messages, phone calls, emails, and social media platforms, they leave various records on their devices and the Internet, forming a huge social network. Community detection can help investigators analyze group leaders and community structure, which is significant to case investigation and crime control. This paper proposes an unsupervised community detection framework based on GCN (Graph Convolution Network). Our main idea is to utilize social network topology and social network communication content to construct user features. The proposed end-to-end community detection framework can display the social network topology, locate the core members of the community, and show the connections between users. We evaluate our framework on the Enron email dataset. Experimental results show that our model outperforms unsupervised benchmark methods. We also concluded that the community detection framework should be able to analyze social networks, enabling forensic investigators to reveal connections between people.