基于模糊的社会网络优化,特别涉及恐怖分子网络的挖掘

Suraksha Tiwari, Shilky Shrivastava, Manish Gupta
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引用次数: 2

摘要

社会网络分析的广阔领域促使执法机构对社会网络行为进行研究。世界各国的执法机构已经开始研究先进的知识发现技术,以协助分析恐怖分子的信息。这类技术的使用被视为通过预测恐怖分子的活动来打击恐怖主义的情报工具。这种恐怖活动可以通过发现网络中的可疑节点来预测。在发现可疑节点的方法上,提出了一种减小网络规模或减少节点个数的方法。由于网络的大小是SNA中新出现的问题。在该方法中,我们使用模糊对网络进行约简,使约简后的网络只包含潜在节点的集合。应用我们的方法得到了一个具有高恐怖分子节点可能性的简化网络。
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Fuzzy based optimization of social network with special reference to terrorist network mining
The vast region of Social network analysis has led the law enforcement agencies to study the behavior of social network. Law enforcement agencies in the world have begun to study on advanced knowledge discovery technologies to assist in the analysis of terrorist's information. The use of such type of technologies is treated as intelligence tools to combat terrorism by anticipating activity of terrorists. This terrorism activity can be predicted by discovering suspicious nodes in the network. In the way of discovering suspicious nodes we propose a method to reduce the size of network or to reduce the no. of nodes in the network because the size of network is emerging problem in SNA. In our method we use fuzzy for reduction of network so that reduced network consists only set of potential nodes. After applying our method we get a reduced network with high possibility of terrorist nodes in network.
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