A Preferential Growing Evolution-model of Terrorist Networks Based on Multi-local Network
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摘要: 恐怖组织网络是一种特殊的复杂网络，其时空演化规律反映出恐怖组织活动的特征.为更准确地理解恐怖组织网络的动态演化规律，提出一种基于多局域的恐怖组织网络择优增长演化模型，并对此模型进行了仿真与模拟.该模型能准确地描述在局部信息条件下，新节点的择优和网络的增长过程及其规律；并且利用网络信息中心度来衡量恐怖组织网络节点的信念水平，动态地刻画了恐怖组织网络的增长过程.实验结果表明：恐怖组织网络的局域度分布仍服从幂律分布，网络信息中心度具有集中与分散性的特征；最后，对多个恐怖组织网络按该模型进行仿真演化，验证了该模型的准确性与科学性.Abstract: The network of terrorist organizations is a special complex network, and its spatiotemporal evolution process reflects the characteristics of terrorist organization activities. To more accurately depict the dynamic evolution process of terrorist network, this paper puts forward a preferential-growing evolution model of terrorist networks based on the multi-local network, and the model is simulated. The model can accurately describe the growth of new nodes and the growth process of network under the condition of local information. We use the node network information center degree to measure the level of terrorist beliefs, dynamic characterization of a terrorist network growth process. The experimental results show:in the terrorist networks, the degree distribution of local network follows the power-law distribution, the network information center degree of the nodes of local network has the characteristics of centralized and decentralized. Finally, the model is verified with the data of terrorist networks. The verification results show that the model is accurate.
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Key words:
- Terrorist networks /
- preferential-growth /
- multi-local network /
- information center degree /
- power law distribution
1) 本文责任编委 赵铁军 -
图 1 恐怖组织网络时空演化过程
Fig. 1 Spatiotemporal evolution of the terrorism organization network
图 3 $N(t)=10\, 000, $ $M=20, 25, $ 30, $m=1, 3, $ 5, 在双对数坐标下, $\delta=0.2, 0.1, 0.05$的局域网络度分布比较图
Fig. 3 The comparison of degree distribution at $\delta=0.2, $ 0.1, 0.05 of the local network, in the log-log scale, for the case that $N(t) = 10 000$, $M = 20, 25, $30 and $m= 1, 3, 5$
图 4 网络($M=25$)增长到10 000个节点时, 在$m=1, 3, 5$条件下, 局域内节点的$C'(n_i)$值比较图
Fig. 4 The comparison of $C'(n_i)$ of local network, at network ($M=25$) growing to $10 000$ nodes with $m = 1, 3, 5$
图 6 "9$\cdot$11"恐怖组织网络择优增长演化过程
Fig. 6 Preferential-growing evolution of "9$\cdot$11" terrorist network
图 7 伦敦爆炸案恐怖组织网络择优增长演化过程
Fig. 7 Preferential-growing evolution of London bombing terrorist network
图 8 马德里火车站爆炸案恐怖组织网络择优增长演化过程
Fig. 8 Preferential-growing evolution of Madrid train bombings terrorist network
图 9 在双对数坐标下, 基于多局域的"9$\cdot$11"恐怖组织网络择优增长模型中局域度分布对比图
Fig. 9 In the log-log scale, the comparison of local network degree distribution of "9$\cdot$11" terrorist network based on multi-local-network preferential-growing model
图 10 在双对数坐标下, 基于多局域的伦敦爆炸案恐怖组织网络择优增长模型中局域度分布对比图
Fig. 10 In the log-log scale, the comparison of local network degree distribution of London bombing terrorist network based on multi-local-network preferential-growing model
图 11 在双对数坐标下, 基于多局域的伦敦爆炸案恐怖组织网络择优增长模型中局域度分布对比图
Fig. 11 In the log-log scale, the comparison of local network degree distribution of London bombing terrorist network based on multi-local-network preferential-growing model
图 12 三个恐怖组织网络演化到1 000个节点时, $C'_i(n_i)$统计对比图
Fig. 12 The statistical comparison of $C'_i(n_i)$, with the three terrorist networks ha"ing grown to 1 000 nodes
图 13 三个恐怖组织网络演化到3 000个节点时, $C'_i(n_i)$统计对比图
Fig. 13 The statistical comparison of $C'_i(n_i)$, with the three terrorist networks ha"ing grown to 3 000 nodes
图 14 三个恐怖组织网络演化到5 000个节点时, $C'_i(n_i)$统计对比图
Fig. 14 The statistical comparison of $C'_i(n_i)$, with the three terrorist networks ha"ing grown to 5 000 nodes
表 1 "9$\cdot$11"恐怖组织网络局域划分
Table 1 The di"ision on local network of "9$\cdot$11" Terrorist Network
$L_j (t)$ $\delta$ 5 0.263 4 0.211 5 0.263 5 0.263 
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表 2 伦敦爆炸案恐怖组织网络局域划分
Table 2 The division on local network of London bombing terrorist network
$L_j (t)$ $\delta$ 5 0.106 7 0.149 7 0.149 10 0.213 18 0.383
下载: 导出CSV
表 3 马德里火车站爆炸案恐怖组织网络局域划分
Table 3 The division on local network of Madrid train bombings terrorist network
$L_j (t)$ $\delta$ 4 0.06 4 0.06 6 0.09 10 0.149 11 0.164 13 0.193 19 0.284
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