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JACIII Vol.16 No.5 pp. 619-630
doi: 10.20965/jaciii.2012.p0619
(2012)

Paper:

Process Estimation of Word-of-Mouth Information Spread Based on Ad Hoc Communications

Daisuke Katagami*, Mizuki Takei**, and Katsumi Nitta**

*Faculty of Engineering, Tokyo Polytechnic University, 1583 Iiyama, Atsugi, Kanagawa 243-0297, Japan

**Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, J2-53, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Japan

Received:
December 10, 2011
Accepted:
April 20, 2012
Published:
July 20, 2012
Keywords:
ad hoc communications, word-of-mouth information, time-series data, human network
Abstract
We focus on the information spread in ad hoc communications, and propose a method of estimating process of word-of-mouth information spread based on analysis of the human network generated by using a contact history among people. This method extracts the cluster structure of people which changes according to the time-series and identifies the clusters including the people which transmitted information. The results of the experiments which applied the proposal method to the data generated by using an agent based simulation method shows that it becomes possible to estimate the information spread process from a connection among the clusters in the human network.
Cite this article as:
D. Katagami, M. Takei, and K. Nitta, “Process Estimation of Word-of-Mouth Information Spread Based on Ad Hoc Communications,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.5, pp. 619-630, 2012.
Data files:
References
  1. [1] K. Takahashi, S. Amamiya, T. Iwao, Z. Guoqiang, and M. Amamiya, “Information Distribution System based on Community and User Profile,” Forum on Information Technology 2003, pp. 49-51, 2003 (in Japanese).
  2. [2] M. E. J. Newman, “The structure and function of complex networks,” SIRM Review, Vol.45, pp 167-256, 2003.
  3. [3] N. Masuda and N. Konno, “Introduction to complex networks,” Sangyo Tosho, 2005.
  4. [4] T. Hayashi, “Agent-based modeling of word-of-mouth in marketing,” National Conf. of The Japan Society for Management Information, pp. 140-143, 2006 (in Japanese).
  5. [5] Y. Maeno and Y. Ohsawa, “An invisible fixer in an organization inferred from communication,” J. of Japanese Society for Artificial Intelligence, Vol.22, No.4, pp. 389-396, 2007 (in Japanese).
  6. [6] Y. Ohsawa, “Data crystallization: chance discovery extended for dealing with unobservable events,” New mathematics and natural science, Vol.1, No.3, pp. 373-392, 2005.
  7. [7] Y. Maeno and Y. Ohsawa, “Stable deterministic crystallization for discovering hidden hubs,” Proc. of the IEEE Int. Conf. on Systems, Man & Cybernetics, pp. 1393-1398, 2006.
  8. [8] H. Tanuma and H. Deguchi, “Development of Agent-Based Social Simulation Language: SOARS,” The Institute of Electronics, Information and Communication Engineers, Vol.J90-D, No.9, pp. 2415-2422, 2007 (in Japanese).
  9. [9] M. Ichikawa, Y. Koyama, and H. Deguchi, “Simulation Model based on sphere of influence in station,” Japan Association of Simulation and Gaming 2006, pp. 19-22, 2006 (in Japanese).
  10. [10] M. Girvan and M. E. J. Newman, “Community structure in social and biological networks,” PNAS, Vol.99, No.12, pp. 7821-7826, 2002.
  11. [11] M. Ichikawa, Y. Koyama, and H. Deguchi, “A Basic City Simulation Model for Evaluating Social Phenomena,” Agent-Based Approaches in Economic and Social Complex Systems IV, Springer Japan, pp. 71-78, 2007.

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Last updated on Apr. 22, 2024