This paper uses an extensive network approach to “East Turkistan” activities by building both the one-mode and the bipartite networks for these activities. In the one-mode network, centrality analysis and spectrum analysis are used to describe the importance of each vertex. On this basis, two types of core vertices — The center of communities and the intermediary vertices among communities — are distinguished. The weighted extreme optimization (WEO) algorithm is also applied to detect communities in the one-mode network. In the “terrorist-terrorist organization” bipartite network, the authors adopt centrality analysis as well as clustering analysis based on the original bipartite network in order to calculate the importance of each vertex, and apply the edge clustering coefficient algorithm to detect the communities. The comparative and empirical analysis indicates that this research has been proved to be an effective way to identify the core members, key organizations, and communities in the network of “East Turkistan” terrorist activity. The results can provide a scientific basis for the analysis of “East Turkistan” terrorist activity, and thus provide decision support for the real work of “anti-terrorism”.
['Julei Fu', 'Ying Fan', 'Yang Wang', 'Shouyang Wang']
NETWORK ANALYSIS OF TERRORIST ACTIVITIES