Research on historical phase division of terrorism: An analysis method by time series complex network

Anti-terrorism research is an important academic topic in current societies. The crucial features of attacked incidents can be obtained effectively by identifying phase division of terrorism history. To handle time-series issues, complex networks theories are efficient and reliable analysis solutions. Therefore, we propose an original community detection method for complex time-series networks. Especially, we consider the improved local density operator and bi-directional neighbor retrieval (ILD-BNR). First, complex networks of threatened countries are established by incidents feature and time-series principles. Then, cores of networks are selected by improved density operator. After that, attributes of unstable nodes are revised iteratively until initialization is finished. The optimal classification results are obtained by retrieval pattern of bi-directional neighbor. Finally, on the basis of clustering consequences, historical phases are divided ultimately. The mechanism of each phase is discussed simultaneously. The experiments demonstrate some important conclusions: a) The accuracy of proposed method is better than other evaluated algorithms on real time-series networks; b) The historical phase number is reduced reasonably, which is beneficial to analysis of information; and c) Classification consequences can reflect the historical tendency of terrorism. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The proposed method for complex time-series networks involves establishing networks, selecting core nodes, revising attributes of unstable nodes, and classifying historical phases based on clustering consequences. Experimental results show that the method outperforms other evaluated algorithms in terms of accuracy, reduces the number of historical phases reasonably, and reflects the historical tendency of terrorism through classification results.