Social Network Analysis of Terrorist Networks
Ashlesha S. Nagdive1, Rajkishor Tugnayat2, Atharva Peshkar3
1Ashlesha S. Nagdive*, Information Technology, G H Raisoni College of Engineering, Nagpur, India.
2Dr. Rajkishor Tugnayat, Principal, Shri Shankarprasad Agnihotri College of Engineering, Wardha, India.
3Atharva Peshkar, Information Technology, G.H. Raisoni College of Engineering, Nagpur, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2553-2559 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5431029320/2020©BEIESP | DOI: 10.35940/ijeat.C5431.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Terrorist Activities worldwide has led to the development of sophisticated methodologies for analyzing terrorist groups and networks. Ongoing and past research has found that Social Network Analysis (SNA) is most effective method for predictive counter-terrorism. Social Network Analysis (SNA) is an approach towards analyzing the terrorist networks to better understand the underlying structure of a network and to detect key players within the network and their links throughout the network. It is also need of the hour to convert available raw data into valuable information for the purpose of global security. Comparative study among SNA tools testify their applicability and usefulness for data gathered through online and offline social sources. However it is advised to incorporate temporal analysis using data mining methods, to improve the capability of SNA tools to handle dynamic social media data. This paper examine various aspects of Social Network Analysis as applied to terrorism, taking empirical data, and open source data based studies into account. This work primarily focuses on different types of decentralized terrorist networks and nodes. The nodes can be classified as organizations, places or persons. We take help of varied centrality measures to identify key players in this network.
Keywords: Social Network Analysis, Terrorist Networks, Counter-Terrorism, Centrality, Investigative Data mining.