Analyzing the Weighted Dark Networks using Scale-Free Network Approach

The task of identifying the main key nodes in the dark (covert) networks is very important for the researchers in the field of dark networks analysis. This analysis leads to locate the major nodes in the network as the functionality can be minimized by disrupting major key nodes in the network. In this paper, we have primarily focused on two basic network analysis metrics, degree and betweenness centrality. Traditionally, both these centrality measures have been applied on the bases of number of links connected with the nodes but without considering link weights. Like many other networks, dark networks also follow scale-free behavior and thus follow the power-law distribution where few nodes have maximum links. This, inhomogeneous structure of network causes the creation of key nodes. In this research, we analyze the behavior of nodes in dark networks based on degree and betweenness centrality measures by using 9/11 terrorist network dataset. We analyzed both these measures with weighted and un-weighted links to prove that weighted networks are much closer to scale-free phenomenon as compared to un-weighted networks. Key-Words: Dark networks; power-law distribution; scale-free networks; node degree centrality; betweenness centrality, weighted network analysis.