Analysis of terrorist social networks with fractal views

Abstract

Social network visualization has drawn significant attention over recent years. It creates images of social networks that provide investigators with new insights about network structures and helps them to communicate those insights to others. Visualization facilitates the social network analysis. It supports the investigators to discover patterns of interactions among the social actors including detecting subgroups, identifying central actors and their roles, and discovering patterns of interactions among social actors. However, visualizing a large heterogeneous social network has several challenges. The large size of networks, complex relations among social actors and limited number of available pixels on a screen make it difficult to present important information clearly to investigators and hence reduce the capability of investigators to explore the networks. In this work, we propose the fractal views to construct a visual abstraction of a large and complex social network with users selected social actors as focuses. The fractal views are focus and context visualization techniques using an information reduction approach. It controls the amount of information displayed by focusing on the syntactic structure of information. It is useful in discovering knowledge from terrorist social networks for combating the war on terrorism. Such application has formed an important research topic, known as intelligence and security informatics, in recent years due to the terrorist attacks of September 11 2001 (9/11) and several other terror attacks that have occurred within the last decade. We present several case studies to demonstrate the capability of the proposed technique on analyzing the Global Salafi Jihad terrorist social network. It extracts the hidden relationships among terrorists through user interactions. In addition, we have conducted a user evaluation to assess the efficiency and effectiveness of fractal views. It shows that fractal views outperform fisheye views and zoom-in windows to support users in visualizing and analyzing terrorist social networks.