Scientific Communities Found Based on the Path Structure of Citation Network
Xiao Xiao, Song Cao, Lan Huang, Yutian Tang
DOI: 10.4236/ijis.2012.21003   PDF    HTML     3,831 Downloads   9,332 Views   Citations


Based on the structure of citation network, the citation paths among papers, and the association strength such as coupling, co-citation and etc. between two papers are defined in this article. We give formulas to quantify the association strength in order to establish citation network model based on the citation path structure. Then, the OPTICS algorithm is brought into the scientific communities found model since it can solve the parameter’s setting problem. This method combines various kinds of path structures together and thus it contains more complete citation network information. Experiments and analysis reveal the reliability and validity of this method.

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X. Xiao, S. Cao, L. Huang and Y. Tang, "Scientific Communities Found Based on the Path Structure of Citation Network," International Journal of Intelligence Science, Vol. 2 No. 1, 2012, pp. 16-21. doi: 10.4236/ijis.2012.21003.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] X. M. Liu, J. Bollen and M. Nelson, “Co-Authorship Networks in the Digital Library Research Community,” Information Processing and Management, Vol. 41, No. 5, 2005, pp. 1462-1480. doi:10.1016/j.ipm.2005.03.012
[2] R. Ichise, H. Takeda and T. Muraki, “Research Community Mining with Topic Identification,” Proceedings of the Information Visualization, London, 2006, pp. 276- 281.
[3] H. X. Lin, “Scientific Group Mining Based on the Citation Network,” Master Dissertation, Fudan University, Shanghai, 2009.
[4] N. Ma and J. C. Guan, “Survey on the Mining Algorithm Based on the Network Structure,” Informatics Disciple, Vol, 13, No. 1, 2008, pp. 3-14.
[5] K. B?rner, L. Dall’Asta, W. Ke and A. VeSPignani, “Studying the Emerging Global Brain: Analyzing and Visualizing the Impact of Co-Authorship teams,” Complexity, Vol. 10, No. 4, 2005, pp. 57-67. doi:10.1002/cplx.20078
[6] M. J. Bommarito, D. M. Katz, J. L. Zelner and J. H. Fowler,” Distance Measures for Dynamic Citation Networks,” Elsevier, Amsterdam, Vol. 289, No. 19, 2010, pp. 4201-4208.
[7] Y. Takeda and Y. Kajikawa, “Tracking Modularity in Citation Networks,” Scientometrics, Vol. 82, No. 3, 2010, pp. 783-792. doi:10.1007/s11192-010-0158-z
[8] G. Z. Wang and G. L. Wang, “Improved Quick DBSCAN Algorithm,” Journal of Computer Applications, Vol. 26, No. 15, 2009, pp. 2364-2373.
[9] S. K. Zhou, A. Y. Zhou and J. Cao, “DBSCAN Algorithm Based on Data Partition,” Journal of Computer Research and Development, Vol. 2, No. 2, 2000, pp. 169-194.
[10] H.-P. Kriege and M. Pfeifle, “Density-Based Clustering of Uncertain Data,” Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, New York, 2005. doi:10.1145/1081870.1081955
[11] J. Liu and X. S. Ma, “Improved DBSCAN Clustering Algorithm’s Study and Application,” Communication and Computer, China, 2008.
[12] Y. L. Zeng, H. B. Xu and S. Bai, “Improved OPTICS Algorithm and Its Application in Text Clustering,” Journal of Chinese Information Processing, Vol. 9, No. 4, 2008, pp. 1483-1492.

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