Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
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Analysis of Network Chain using Dynamic Convolution Model

동적 확률 재규격화를 이용한 네트워크 연쇄 관계 해석

  • 이형진 (서울대학교 농업생명과학대학 생태 조경.지역시스템공학부) ;
  • 김태곤 (서울대학교 농업생명과학대학 생태 조경.지역시스템공학부) ;
  • 이정재 (서울대학교 농업생명과학대학 조경.지역시스템공학부, 농업생명과학연구원) ;
  • 서교 (서울대학교 농업생명과학대학 조경.지역시스템공학부, 농업생명과학연구원, 그린바이오과학기술 연구원)
  • Received : 2013.11.11
  • Accepted : 2013.12.10
  • Published : 2014.01.31
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Abstract

Many classification studies for the community of densely-connected nodes are limited to the comprehensive analysis for detecting the communities in probabilistic networks with nodes and edge of the probabilistic distribution because of the difficulties of the probabilistic operation. This study aims to use convolution method for operating nodes and edge of probabilistic distribution. For the probabilistic hierarchy network with nodes and edges of the probabilistic distribution, the model of this study detects the communities of nodes to make the new probabilistic distribution with two distribution. The results of our model was verified through comparing with Monte-carlo Simulation and other community-detecting methods.

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References

  1. Burt, J. M. and M. B. Garman, 1971. Conditional Monte Carlo: A simulation technique for stochastic network analysis. Management Science 18(3): 207-217. https://doi.org/10.1287/mnsc.18.3.207
  2. Dechter, R., and J. Pearl, 1989. Tree clustering for constraint networks. Artificial Intelligence 38(3): 353-366. https://doi.org/10.1016/0004-3702(89)90037-4
  3. Fefferman, C., 1970. Inequalities for strongly singular convolution operators. Acta Mathematica 124(1): 9-36. https://doi.org/10.1007/BF02394567
  4. Friedman, N.. 2004. Inferring Cellular Networks Using Probabilistic Graphical Models. Science 303.5659: 799-805. https://doi.org/10.1126/science.1094068
  5. Friedman, N., D. Geiger, and M. Goldszmidt, 1997. Bayesian Network Classifiers. Machine learning 29 (2-3): 131-163. https://doi.org/10.1023/A:1007465528199
  6. Girvan, M. and M. E. J Newman, 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99(12): 7821-7826. https://doi.org/10.1073/pnas.122653799
  7. Gudkov, V., J. E. Johnson, and S. Nussinov, 2002. Graph equivalence and characterization via a continuous evolution of a physical analog. arXiv preprint condmat/ 0209112.
  8. Leskovec, J., K. J. Lang, and A. Dasgupta, 2007. Community structure in large networks: Natural cluster sizes and the absence of large well-defined clusters. Internet Mathematics 6(1): 29-123.
  9. Leskovec, J., K. J. Lang, and M. Mahoney, 2010. Empirical comparison of algorithms for network community detection. Proceedings of the 19th international conference on World wide web, 631-640. New York, USA.
  10. Marcos, G. Q., L. Zhao, L. Ronaldo, and A. F. Roseli, 2008. Particle competition for complex network community detection. Chaos: An Interdisciplinary. Journal of Nonlinear Science 18(3): 033107-033107.
  11. Newman, M. E. J. and M. Girvan, 2004. Finding and evaluating community structure in networks. Physical Review E. 69(2): 026113. https://doi.org/10.1103/PhysRevE.69.026113
  12. O'Neil, R., 1963. Convolution operators and spaces. Duke Mathematical Journal 30(1): 129-142. https://doi.org/10.1215/S0012-7094-63-03015-1
  13. Peter, J. M., T. Richardson, K. Macon, A.P. Mason, and J. Onnela, 2010. Community Structure in Time- Dependent, Multiscale, and Multiplex Networks. Science 328(5980): 876-878. https://doi.org/10.1126/science.1184819
  14. Pothen, A., H. D. Simon, and K. P. Liou, 1990. Partitioning sparse matrices with eigenvectors of graphs. Journal on Matrix Analysis and Applications 11(3): 430-452. https://doi.org/10.1137/0611030
  15. Reichardt, J. and S. Bornoholdt, 2004. Detecting fuzzy community structures in complex networks with a Potts model. Physical Review Letters 93(21): 218701. https://doi.org/10.1103/PhysRevLett.93.218701
  16. Reichardt, J. and S. Bornoholdt, 2006. Statistical mechanics of community detection. Physical Review Letters 74(1): 016110.
  17. Ringer, L. J., 1971. A statistical theory for PERT in which completion times of activities are inter-dependent. Management Science 17(11): 717-723. https://doi.org/10.1287/mnsc.17.11.717
  18. Romualdo, P. and V. Alessandro, 2001. Epidemic spreading in scale-free networks. Statistical Mechanics 86(14): 3200-3203.
  19. Roy, S., D. Saha, D. Bandyopadhyay, T. Ueda, and S. Tanaka, 2003. A network-aware MAC and routing protocol for effective load balancing in ad hoc wireless networks with directional antenna. Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing 88-97. New York, USA.
  20. Wu, F. and B.A. Huberman, 2004. Finding communities in linear time: a physics approach. The European Physical Journal B 38(2): 331-338. https://doi.org/10.1140/epjb/e2004-00125-x
  21. Zhou, H., 2003. Distance, dissimilarity index, and network community structure. Physical Review E. 67(6): 061901. https://doi.org/10.1103/PhysRevE.67.061901