An efficient concept generation approach to identifying most influential node in a Terrorist Network using Weighted Formal Concept Analysis. (2020)

• Record Type: Journal Article
• Authors: Kumar Mishra, Amit; Joshi, Nisheeth; Mathur, Iti
• Is Part Of: Materials today. Volume 29:Part 2(2020)
• Page Start: 427
• Page End: 439
• Publication Date: 2020
• Subjects: WFCA -- Social Network Analysis -- Centrality Measures -- Influential Node; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2020.07.296
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: In recent years the Terrorist network research community has collected huge information for the appearance of composite and disparate communication patterns in complex terrorist social network. The identification of most centralized node(s) is crucial to understand the information flow and their communicable spread. Strategies for identifying influential nodes in complex terrorist network have been of interest. Techniques have been proposed from different perspectives, each with its own particular favourable circumstances and deficiency. Current study based on Formal Concept Analysis with Weight, under Terrorist Network Mining to identify the node(s) in a Terrorist Network, who influenced other members of the terrorist group. It may help to destabilize the terrorist network more effectively by removing or destroying the all communication link of higher ranking node(s). First we construct the adjacency matrix of 26/11 Mumbai Terrorist Attack network as the formal context. Next, we calculate the all possible concepts of the formal context. Subsequently the weight of every node within the terrorist network calculated and ranked each node accordingly. A comparison has been made of result with other well-known centrality algorithms like closeness centrality, node betweenness centrality, flow betweeness, PageRank, Katz, Reach centrality and PN centrality