Name
Title | ||
|---|---|---|
Analysis of the Terrorist Organization Alliance Network Based on Complex Network Theory. |
Abstract | ||
|---|---|---|
Preventing terrorist attacks is crucial to the development and safety of a country. Therefore, studying the law and the occurrence of terrorist attacks enable the countries to develop steadily. Specially, we have noticed that over time, terrorism has gradually shown the characteristics that are manifested as vast and hidden geographical distribution and can be modeled as networks. Based on such characteristics, the traditional method of analyzing individual terrorist organizations no longer serves the needs of anti-terrorism security research under the new situation. Since the terrorist organizations can be modeled as networks, terrorists use their associated networks, which is aimed to achieve the greater and more rapid criminal activity. In view of the above characteristics of the terrorist organization in the new era, in this paper, we introduce the social network theory to analyze the regular patterns of the terrorist attacks. First, we analyze the terrorist attacks in the data set. If two or more terrorist organizations participate in the certain terrorist attack at the same time, they will establish a side-by-side relationship and increase the weight of one side. In this way, the terrorist organization network is constructed and the statistical analysis can be carried out. Then, we divide the organization of the terrorist organization network by means of community division method and the terrorist organization is classified in detail. Here, the classification of terrorist organizations into 13 categories laid the foundation for subsequent analysis. The experimental results verify the effectiveness and efficiency of the proposed analysis. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2929798 | IEEE ACCESS |
Keywords | DocType | Volume |
Terrorist attacks,complex network,power law,terrorist organization alliance network,community structure | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guijun Li | 1 | 0 | 0.34 |
| Jun Hu | 2 | 0 | 1.69 |
| Yanqiu Song | 3 | 0 | 0.34 |
| Yingxuan Yang | 4 | 0 | 0.34 |
| Huijia Li | 5 | 0 | 0.68 |