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Communication network data, such as the volume of traffic and the number of users, has been mainly used for facility engineering of communication networks. This use focuses on quantitative data. The qualitative data on a communication network can be seen as analogous to the characteristics of human activity in society. If such sociological information can be extracted from communication network data, it would be possible to develop technology that supports enterprises in developing their marketing strategies. We have found that the power law is applicable to the volume of cellular phone traffic and the number of SNS users, and, using this property, we have identified the structure of a social network used for the exchange of information between SNS users. In this paper, we investigate the characteristics of the communication frequency on links in social networks by using the power law observed in the process of computer virus infection, and propose an information propagation model. Our simulation result shows that the proposed information propagation model exhibits characteristics similar to those in the data obtained from a real SNS.
Date of Conference: 14-17 July 2008