 **Analyzing covert social network foundation behind terrorism disaster** 

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## 2.3 Computational Data Processing

Our algorithm focuses on inter-cluster relationships in a social network (Ohsawa, 2005). Examples of the inter-cluster relationships include sharing of information on the guard system among the hijacker groups via a conspirator, or efficient multicast of a directive to the groups from a conspirator. The input of the algorithm is the observed records in eq.(1). The output is the ranking of the individual records (indicating suspicious inter-cluster relationships or unobserved persons playing a catalyst role among the clusters), and the persons in the clusters playing a gateway role to the unobserved person. The output is further processed to draw a social network diagram. 

As a preparation, we define a simple Boolean function B(s) by eq.(2). It returns 1 if the statement s is true, and 0 otherwise. 

## =Otherwise If Is True 0 1 ( )S B S . (2)

At first, the all persons appearing in the observed records bi in eq.(1) are grouped into clusters cj**. The number of clusters |c| depends on the prior knowledge. Mutually close** 
persons form a cluster. The measure of closeness between a pair of persons is evaluated by Jaccard's coefficient. It is defined by eq.(3). The function F(pi**) is the occurrence** frequency of a person pi **in the records. The closeness means activeness of the** 
communication if the record is a set of the persons appearing together in the emails, conversations, or meetings. Jaccard's coefficient is used widely in link discovery, web mining, or text processing. 