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Analysis of Assortativity in the Keyword-based Patent Network Evolution

키워드기반 특허 네트워크 진화에 따른 동종성 분석

  • Received : 2013.07.30
  • Accepted : 2013.11.09
  • Published : 2013.12.31

Abstract

Various networks can be observed in the world. Knowledge networks which are closely related with technology and research are especially important because these networks help us understand how knowledge is produced. Therefore, many studies regarding knowledge networks have been conducted. The assortativity coefficient represents the tendency of connections between nodes having a similar property as figures. The relevant characteristics of the assortativity coefficient help us understand how corresponding technologies have evolved in the keyword-based patent network which is considered to be a knowledge network. The relationships of keywords in a knowledge network where a node is depicted as a keyword show the structure of the technology development process. In this paper, we suggest two hypotheses basedon the previous research indicating that there exist core nodes in the keyword network and we conduct assortativity analysis to verify the hypotheses. First, the patents network based on the keyword represents disassortativity over time. Through our assortativity analysis, it is confirmed that the knowledge network shows disassortativity as the network evolves. Second, as the keyword-based patents network becomes disassortavie, clustering coefficients become lower. As the result of this hypothesis, weconfirm the clustering coefficient also becomes lower as the assortative coefficient of the network gets lower. Another interesting result concerning the second hypothesis is that, when the knowledge network is disassorativie, the tendency of decreasing of the clustering coefficient is much higher than when the network is assortative.

우리가 살고 있는 세계에는 다양한 네트워크들이 발견된다. 특히, 기술 및 학문과 밀접하게 관련 있는 지식 네트워크는 지식이 생산되는 방식을 이해하는데 도움을 주기 때문에 큰 의미를 갖는다. 이러한 중요성을 바탕으로 지금까지 지식 네트워크를 대상으로 한 많은 네트워크 분석들이 이루어져 오고 있다. 그 중에서 동종성 계수는 네트워크 내의 노드들이 비슷한 성향을 가진 노드들과 연결을 맺으려는 경향 수치로 나타낸다. 동종성 계수가 가지는 이러한 특성은 지식 네트워크로 간주 될 수 있는 키워드기반 특허 네트워크에서 기술이 어떻게 진화하는지 확인 하는데 도움을 줄 수 있다. 왜냐하면 지식 내트워크내 노드로 표현되는 키워드들 간의 관계들이 기술이 만들어지는 구조를 나타내기 때문이다. 본 연구에서는 키워드 네트워크에는 핵심 노드가 존재한다는 기존 연구 결과를 기반으로 두 가지 가설을 세우고 이에 대한 검증으로 동종성 분석을 수행 하였다. 첫 번째 가설은 키워드 기반 특허 네트워크는 시간 흐름에 따라 비동종성을 띌 것으로 예측 하며, 동종성 분석을 통해 특허 네트워크가 진화함에 따라 비동종성을 보이는 것을 확인 하였다. 다음으로, 키워드 기반 특허 네트워크가 비동종성을 보일수록 클러스터링 계수 또한 낮아 질 것으로 예측하는 두 번째 가설에 대한 동종성 분석 결과, 네트워크의 동종성 계수가 낮아질수록 클러스터링 계수 또한 낮아진다는 사실을 확인 할 수 있었다. 또한, 두 번째 가설의 검증과정에서 확인 한 흥미로웠던 결과로써, 동종성 계수가 감소함에 따라 클러스터링 계수가 낮아지는 정도는 네트워크가 동종성을 보일 때 보다 비동종성을 보일 때가 훨씬 높았다.

Keywords

References

  1. Abe, S., Suzuki, N., "Scaling relation for earthquake networks," Phys. Rev. E, 74, 026113, 2006 https://doi.org/10.1103/PhysRevE.74.026113
  2. Albert, R., A.-L. Barabasi, "Statistical mechanics of complex networks," Reviews of Modern Physics, Vol.74 No.1, pp. 47-97, 2002 https://doi.org/10.1103/RevModPhys.74.47
  3. Albert, R., I. Albert, G. Nakarado, "Structural vulnerability of the North American power grid," Physical Review E, Vol.69 No.2, 2004
  4. Badham, J., R. Stocker, "The impact of network clustering and assortativity on epidemic behaviour," Theor Popul Biol, Vol.77 No.1, pp. 71-75, 2010 https://doi.org/10.1016/j.tpb.2009.11.003
  5. Bagler, G., "Analysis of the airport network of India as a complex weighted network," Physica A: Statistical Mechanics and its Applications, Vol.387 No.12, pp. 2972-2980, 2008 https://doi.org/10.1016/j.physa.2008.01.077
  6. Batagelj, V., A. Mrvar, "Some analyses of Erdos collaboration graph", Social Networks, Vol.22 No.2: pp. 173-186, 2000 https://doi.org/10.1016/S0378-8733(00)00023-X
  7. Choi, J., Kim, H., Im, N., "Keyword network analysis for technology forecasting", Journal of Intelligence and Information Systems, Vol.17 No.4, pp.227-240, 2011
  8. Choi, J., N. Im,H. Kim, Y.-S. Hwang, "A study on the patent analysis for effective technology forecasting," Information Science and Digital Content Technology (ICIDT), 2012 8th International Conference on, 2012
  9. DJ, W., S. SH., "Collective dynamics of 'small-world' networks," Nature, Vol.393 No.6684, pp.440-442, 1998 https://doi.org/10.1038/30918
  10. Eisenberg, E., E. Levanon, "Preferential Attachment in the Protein Network Evolution," Physical Review Letters, Vol.91 No.13, 2003
  11. Fabac, R., M. Schatten, T. Duricin, "Social Network Mixing Patterns In Mergers & Acquisitions - A Simulation Experiment," Business Systems Research, Vol.2 No.1, pp. 36-44, 2011 https://doi.org/10.2478/v10305-012-0018-9
  12. Frantzi, K., S. Ananiadou, H. Mima, "Automatic recognition of multi-word terms: the C-value/NC-value method," International Journal on Digital Libraries, Vol.3 No.2, pp. 115-130, 2000 https://doi.org/10.1007/s007999900023
  13. Hu, H.-B., X.-F. Wang, "Disassortative mixing in online social networks," EPL (Europhysics Letters), Vol.86 No.1: 18003., 2009 https://doi.org/10.1209/0295-5075/86/18003
  14. Hummon, N. P., P. Dereian, "Connectivity in a citation network: The development of DNA theory," Social Networks, Vol.11 No.1, pp.39-63, 1989 https://doi.org/10.1016/0378-8733(89)90017-8
  15. Klemm, K., V. M. Eguiluz, "Highly clustered scale-free networks," Physical Review E, Vol.65 No.3: 036123, 2002 https://doi.org/10.1103/PhysRevE.65.036123
  16. Li, X., H. Chen, Z. Huang, M. C. Roco, "Patent citation network in nanotechnology (1976-2004)," Journal of Nanoparticle Research, Vol.9 No.3,: pp. 337-352, 2007 https://doi.org/10.1007/s11051-006-9194-2
  17. Lusseau, D., M. E. Newman, "Identifying the role that animals play in their social networks," Proc Biol Sci 271 Suppl 6, pp. S477-481, 2004 https://doi.org/10.1098/rsbl.2004.0225
  18. Murai, F., D. R. Figueiredo., "Assortative Mixing in BitTorrent-Like Networks". INFOCOM Workshops 2009, IEEE., 2009
  19. Newman, M. E. J., "Assortative Mixing in Networks." Physical Review Letters, Vol.89 No.20: 208701, 2002 https://doi.org/10.1103/PhysRevLett.89.208701
  20. Newman, M. E., D. J. Watts, S. H. Strogatz, "Random graph models of social networks." Proc Natl Acad Sci U S A 99 Suppl 1, pp. 2566-2572, 2002 https://doi.org/10.1073/pnas.012582999
  21. Soffer, S., A. Vazquez, "Network clustering coefficient without degree-correlation biases." Physical Review E, Vol.71 No.5, 2005
  22. Sohn, D.W. Social network analysis, "Kyoungmoon publishing company, 2002
  23. von Wartburg, I., T. Teichert, K. Rost, "Inventive progress measured by multi-stage patent citation analysis." Research Policy, Vol.34 No.10, pp. 1591-1607, 2005 https://doi.org/10.1016/j.respol.2005.08.001
  24. Yi, S., J. Choi, "The organization of scientific knowledge: the structural characteristics of keyword networks." Scientometrics, Vol.90 No.3, pp. 1015-1026, 2011
  25. Yoon, B., Y. Park, "A text-mining-based patent network: Analytical tool for high-technology trend." The Journalof High Technology Management Research, Vol.15 No.1, pp. 37-50, 2004 https://doi.org/10.1016/j.hitech.2003.09.003