Multimode co-clustering for analyzing terrorist networks

Ahmed Aleroud, Aryya Gangopadhyay

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The phenomenon of terrorism is deemed one of the fundamental challenges in national security. Creating defensive technologies to mitigate terrorist attacks requires a simultaneous investigation of contextual relationships among their various dimensions. We proposed and evaluated a graph-based methodology to analyze terrorist networks through co-clustering in a multimode basis. Since there are many heterogeneous relationships in terrorist networks depending on the dimensions used during analysis, we utilized the clustering indicators of the multimode structure discovered in bi- and multimode graphs. Objects and activities that co-occur during terrorist attacks are identified by applying conventional clustering on those indicators. The novelty of our method is in the incremental creation of the multimode structure using its bi-mode counterparts. Our approach is evaluated using these measures: clustering stability and association confidence. The experimental results yields encouraging results in terms of simultaneous clustering of heterogeneous objects in terrorist networks.

Original languageEnglish (US)
Pages (from-to)1053-1074
Number of pages22
JournalInformation Systems Frontiers
Volume20
Issue number5
DOIs
StatePublished - Oct 1 2018
Externally publishedYes

Keywords

  • k-means
  • Multimode clustering
  • Singular value decomposition
  • Social network analysis
  • Terrorist networks

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Multimode co-clustering for analyzing terrorist networks'. Together they form a unique fingerprint.

Cite this