Extracting Skeleton of the Global Terrorism Network Based on m-Modified Topology Potential
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 re...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/7643290 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832568542659084288 |
|---|---|
| author | Shuliang Wang Kanokwan Malang Hanning Yuan Aniwat Phaphuangwittayakul Yuanyuan Lv Matthew David Lowdermilk Jing Geng |
| author_facet | Shuliang Wang Kanokwan Malang Hanning Yuan Aniwat Phaphuangwittayakul Yuanyuan Lv Matthew David Lowdermilk Jing Geng |
| author_sort | Shuliang Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-02f1f2b073594c3893d5b3bc32ce5583 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-02f1f2b073594c3893d5b3bc32ce55832025-02-03T00:58:44ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/76432907643290Extracting Skeleton of the Global Terrorism Network Based on m-Modified Topology PotentialShuliang Wang0Kanokwan Malang1Hanning Yuan2Aniwat Phaphuangwittayakul3Yuanyuan Lv4Matthew David Lowdermilk5Jing Geng6School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaInternational College of Digital Innovation, Chiang Mai University, Chiang Mai 50200, ThailandSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaSkeleton 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.http://dx.doi.org/10.1155/2020/7643290 |
| spellingShingle | Shuliang Wang Kanokwan Malang Hanning Yuan Aniwat Phaphuangwittayakul Yuanyuan Lv Matthew David Lowdermilk Jing Geng Extracting Skeleton of the Global Terrorism Network Based on m-Modified Topology Potential Complexity |
| title | Extracting Skeleton of the Global Terrorism Network Based on m-Modified Topology Potential |
| title_full | Extracting Skeleton of the Global Terrorism Network Based on m-Modified Topology Potential |
| title_fullStr | Extracting Skeleton of the Global Terrorism Network Based on m-Modified Topology Potential |
| title_full_unstemmed | Extracting Skeleton of the Global Terrorism Network Based on m-Modified Topology Potential |
| title_short | Extracting Skeleton of the Global Terrorism Network Based on m-Modified Topology Potential |
| title_sort | extracting skeleton of the global terrorism network based on m modified topology potential |
| url | http://dx.doi.org/10.1155/2020/7643290 |
| work_keys_str_mv | AT shuliangwang extractingskeletonoftheglobalterrorismnetworkbasedonmmodifiedtopologypotential AT kanokwanmalang extractingskeletonoftheglobalterrorismnetworkbasedonmmodifiedtopologypotential AT hanningyuan extractingskeletonoftheglobalterrorismnetworkbasedonmmodifiedtopologypotential AT aniwatphaphuangwittayakul extractingskeletonoftheglobalterrorismnetworkbasedonmmodifiedtopologypotential AT yuanyuanlv extractingskeletonoftheglobalterrorismnetworkbasedonmmodifiedtopologypotential AT matthewdavidlowdermilk extractingskeletonoftheglobalterrorismnetworkbasedonmmodifiedtopologypotential AT jinggeng extractingskeletonoftheglobalterrorismnetworkbasedonmmodifiedtopologypotential |