PEVNET: a framework for visualization of criminal networks
Amer Rasheed, Uffe Kock Wiil · Advances in Social Networks Analysis and Mining · 2014
No major criminal activity is possible without a comprehensive plot behind it. Detecting and understanding criminal activity has been a challenging task for the researchers in criminal networks. One important way of addressing those challenges has been visualization of criminal networks. We propose a framework called PEVNET in which existing visualization techniques for criminal networks are re-designed from a different perspective. Visualization features by way of merging, linking, and grouping of entity attributes is provided to criminal network investigators. Furthermore, we believe that the prevailing challenges to information visualization can be eliminated to a large extent by detecting evolving network patterns, which are extracted by way of visual analysis of criminal activity based on temporal data. Finally, the proposed framework will indicate the most central person in the network in a unique way, which will support the investigators' decision making.