Behavioral Profile Generation for 9/11 Terrorist Network Using Efficient Selection Strategies
In recent days terrorism poses a threat to homeland security. It’s highly motivated by the “net-war” where the extremist are organized in a network structure. The major problem faced is to automatically identify the key player who can maximally influence other nodes in a large relational covert network. The nodes and links are represented in the form of a directional semantic graph where each node is related with more than one relationship with the other node. The behaviors of nodes are analyzed based on the semantic profile generated. This analysis helps the crime analyst to judge the key player for a criminal activity. The semantic profile is obtained by choosing carefully the path types that suits best for a specific node. The selection strategies can be generalized as path equivalence and constraint based. The strategy further supports the variable relaxation approach by grouping all the paths with the same sequence of relations as a single path type. This can also be made as user friendly by letting the user to represent their own preferences on the nodes and links.
KeywordsSocial Network Analysis (SNA) Terrorism Unsupervised learning Selection Strategy Semantic profile
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