Open AccessSocial Network Analysis of Terrorist Networks
Author(s) -
Ashlesha S. Nagdive,
Rajkishor Tugnayat,
Atharva Peshkar
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.c5431.029320
Subject(s) - centrality , social network analysis , terrorism , key (lock) , computer science , social network (sociolinguistics) , raw data , data science , network analysis , organizational network analysis , network science , computer security , social media , data mining , complex network , knowledge management , world wide web , engineering , political science , organizational learning , mathematics , electrical engineering , combinatorics , law , programming language
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.