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Extracting Skeleton of the Global Terrorism Network Based on m-Modified Topology Potential
Joint Authors
Geng, Jing
Yuan, Hanning
Wang, Shuliang
Malang, Kanokwan
Phaphuangwittayakul, Aniwat
Lv, Yuanyuan
Lowdermilk, Matthew David
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-16
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
Skeleton network extraction is a crucial context in studying the core structure and essential information on complex networks.
The objective of this paper is to introduce the novel network extraction method, namely, TPKS-skeleton, for investigating the global terrorism network.
Our method aims to reduce the network’s size while preserving key topology and spatial features.
A TPKS-skeleton comprises three steps: node evaluation, similarity-based clustering, and skeleton network reconstruction.
The importance of skeleton nodes is quantified by the improved topology potential algorithm.
Similarity-based clustering is then integrated to allow detecting high incident concentrations and allocating the important nodes according to the event features and spatial distribution.
Finally, the skeleton network can be reconstructed by aggregating high-influential nodes from each cluster and their simplified edges.
To verify the efficiency of the proposed method, we carry out three classes of a network assessment framework: node-equivalence assessment, network-equivalence assessment, and spatial information assessment.
For each class, various assessment indexes were performed using the original network as a benchmark.
The results verify that our proposed TPKS-skeleton outperforms other competitive methods in particular node-equivalence by Spearman rank correlation and high network structural-equivalence defined by quadratic assignment procedure.
In the spatial perspective, the TPKS-skeleton network preserves reasonably all kinds of spatial information.
Our study paves the way to extract the optimal skeleton of the global terrorism network, which might be beneficial for counterterrorism and network analysis in wider areas.
American Psychological Association (APA)
Wang, Shuliang& Malang, Kanokwan& Yuan, Hanning& Phaphuangwittayakul, Aniwat& Lv, Yuanyuan& Lowdermilk, Matthew David…[et al.]. 2020. Extracting Skeleton of the Global Terrorism Network Based on m-Modified Topology Potential. Complexity،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1143864
Modern Language Association (MLA)
Wang, Shuliang…[et al.]. Extracting Skeleton of the Global Terrorism Network Based on m-Modified Topology Potential. Complexity No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1143864
American Medical Association (AMA)
Wang, Shuliang& Malang, Kanokwan& Yuan, Hanning& Phaphuangwittayakul, Aniwat& Lv, Yuanyuan& Lowdermilk, Matthew David…[et al.]. Extracting Skeleton of the Global Terrorism Network Based on m-Modified Topology Potential. Complexity. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1143864
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1143864