
## **Y. Maeno And Y. Ohsawa** 

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Figure 1 Inset (a) represents the observed records on the participants (pi**) of email-based on-line** 
group discussions. A record is a list of persons who join an individual discussion indicated by the same email subject. Two clusters c0 (p0, p1, p2, p3), and c1 (p4, p5, p6, p7**) can be extracted from the five records. Black nodes denote persons. Black links** 
between the nodes denotes the presence of communication. Inset (b) represents the latent structure (a covert participant) behind the observed records. The fifth record is a clue to infer that an unobserved person px **may be hidden in the empty space between** the gateway persons (p0, p4**) in the clusters. The unobserved person may use telephone** 
to foster communication between the gateway persons. Our aim is to reveal (b) from (a). 

## 2.2 Interactive Process

We propose an interactive process starting from the intelligence, surveillance, and the prior knowledge of expert investigators toward the hypothesis on the latent structure. Figure 2 shows the process. The algorithm, used in the computational data processing shown in the dashed grey box, visualizes the observed records on communication in the form of eq.(1) into a social network diagram. It consists of clustering and ranking procedure. The clustering procedure evaluates the activeness of communication between the persons, and uses the prior knowledge such as the number of groups or the known group leaders. The ranking procedure calculates likeliness of the suspicious inter-cluster relationships, which originates in the unobserved person hidden in the empty spots between the clusters, and indicates the position of the person as a red node. 

The expert investigators explore the difference between the visualized social network diagram and the prior understanding. The difference is expected to be a trigger to notice something new. The expert can update the prior understanding, iterate the above procedures, and finally invent a hypothesis on the latent structure (Maeno, 2007). The details of the algorithm are presented in the following. The essence of the algorithm is the ranking function to calculate likeliness of the suspicious inter-cluster relationships. 