Data Mining in Social Networks and its Application in Counterterrorism
Krishna Ganeriwal1, Gayathri P2, G. Gopichand3, H. Santhi4
1Krishna Ganeriwal*, SCOPE, VIT University, Vellore, India.
2Gayathri P, SCOPE, VIT University, Vellore, India.
3G. Gopichand, SCOPE, VIT University, Vellore, India.
4H Santhi, SCOPE, VIT University, Vellore, India.
Manuscript received on 1 August 2019. | Revised Manuscript received on 5 August 2019. | Manuscript published on 30 September 2019. | PP: 60-68 | Volume-8 Issue-3 September 2019 | Retrieval Number: B2333078219/19©BEIESP | DOI: 10.35940/ijrte.B2333.098319
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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: Social Networks are best represented as complex interconnected graphs. Graph theory analysis can hence be used for insight into various aspects of these complex social networks. Privacy of such networks lately has been challenged and a detailed analysis of such networks is required. This paper applies key graph theory concepts to analyze such social networks. Moreover, it also discusses applications and proposal of a novel algorithm to analyze and gather key information from terrorist social networks. Investigative Data Mining is used for this which is defined as when Social Network Analysis (SNA) is applied to Terrorist Networks to gather useful insights about the network.
Index Terms: Graph Theory, Graph Mining, Investigative Data Mining, Social Network Analysis.
Scope of the Article: Vision-based Applications