Browse > Article
http://dx.doi.org/10.7465/jkdi.2015.26.4.827

Comparison of journal clustering methods based on citation structure  

Kim, Jinkwang (Department of Statistics, Yeungnam University)
Kim, Sohyung (Academic Infrastructure Promotion Team, National Research Foundation of Korea)
Oh, Changhyuck (Department of Statistics, Yeungnam University)
Publication Information
Journal of the Korean Data and Information Science Society / v.26, no.4, 2015 , pp. 827-839 More about this Journal
Abstract
Extraction of communities from a journal citation database by the citation structure is a useful tool to see closely related groups of the journals. SCI of Thomson Reuters or SCOPUS of Elsevier have had tried to grasp community structure of the journals in their indices according to citation relationships, but such a trial has not been made yet with the Korean Citation Index, KCI. Therefore, in this study, we extracted communities of the journals of the natural science area in KCI, using various clustering algorithms for a social network based on citations among the journals and compared the groups obtained with the classfication of KCI. The infomap algorithm, one of the clustering methods applied in this article, showed the best grouping result in the sense that groups obtained by it are closer to the KCI classification than by other algorithms considered and reflect well the citation structure of the journals. The classification results obtained in this study might be taken consideration when reclassification of the KCI journals will be made in the future.
Keywords
Community; journal citation database; KCI; network clustering algorithm;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Levorato, V. and Petermann, C. (2011). Detection of communities in directed networks based on strongly p-connected components. International Conference on Computational Aspects of Social Networks, CASoN, IEEE, 211-216.
2 Leydesdorff, L. (2004). Clusters and Maps of Science Journals Based on Bi-connected Graphs in the Journal Citation Reports. Journal of Documentation, 9, 715-723.
3 Lin, W., Kong, X., Yu, P. S., Wu, Q., Jia, Y. and Li, C. (2012). Community detection in incomplete information networks. In Proceedings of the 21st international conference on World Wide Web, ACM, 341-350.
4 Malliaros, F. D. and Vazirgiannis, M. (2013). Clustering and community detection in directed networks:A survey. Physics Reports Journal, 533, 95-142.   DOI   ScienceOn
5 Narin, F., Carpenter, M. and Berlt, N. (1972). Interrelationships of scientific journals. Journal of the American Society for Information Science, 23, 323-331.   DOI
6 Newman, M. and Girvan, M. (2003). Mixing patterns and community structure in networks. in Statistical Mechanics of Complex Networks, 625, 66-87.   DOI
7 Newman, M. and Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69, 26113.   DOI
8 Newman, M. E. (2004). Fast algorithm for detecting community structure in networks. Physical review E, 69, 066133.   DOI
9 Newman, M. E. and Leicht, E. A. (2007). Mixture models and exploratory analysis in networks. Proceedings of the National Academy of Sciences, 104, 9564-9569.   DOI   ScienceOn
10 Park, C. (2013). Simple principle component analysis using Lasso. Journal of the Korean Data & Information Science Society, 24, 533-541.   DOI   ScienceOn
11 Agarwal, G. and Kempe, D. (2008). Modularity-maximizing graph communities via mathematical programming. The European Physical Journal B-Condensed Matter and Complex Systems, 66, 409-418.   DOI
12 Arenas, A., Duch, J., Fernandez, A. and Gomez, S. (2007). Size reduction of complex networks preserving modularity. New Journal of Physics, 9, 176.   DOI   ScienceOn
13 Blondel, V., Guillaume, J. L., Lambiotte, R. and Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008, P10008.
14 Brandes, U., Delling, D., Gaertler, M., Gorke, R., Hoefer, M., Nikoloski, Z. and Wagner, D. (2008). On modularity clustering. IEEE Transactions on Knowledge and Data Engineering, 20, 172-188.   DOI   ScienceOn
15 Carpenter, M. P. and Narin, F. (1973). Clustering of scientific journals. Journal of the American Society for Information Science, 24, 425-436.   DOI   ScienceOn
16 Pons, P. and Latapy, M. (2006). Computing communities in large networks using random walks. Journal of Graph Algorithms and Applications,, 10, 191-218.   DOI
17 Radicchi, F., Castellano, C., Cecconi, F., Loreto, V. and Parisi, D. (2004). Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of the United States of America, 101, 2658-2663.   DOI   ScienceOn
18 Raghavan, U. N., Albert, R. and Kumara, S. (2007). Near linear time algorithm to detect community structures in large-scale networks. Physical Review E, 76, 036106.   DOI
19 Rosvall, M. and Bergstrom, C. T. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105, 1118-1123.   DOI   ScienceOn
20 Schaeffer, S. E. (2007). Graph clustering. Computer Science Review, 1, 27-64.   DOI   ScienceOn
21 Ward Jr, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American statistical association, 58, 236-244.   DOI   ScienceOn
22 Zhang, A., Ren, G., Cao, H., zhu Jia, B. and bin Zhang, S. (2013). Generalization of label propagation algorithm in complex networks. In Control and Decision Conference (CCDC), 2013 25th Chinese, IEEE, 1306-1309.
23 Zhang, L., Liu, X., Janssens, F., Liang, L. and Glanzel, W. (2010). Subject clustering analysis based on ISI category classification. Journal of Informetrics, 4, 185-193.   DOI   ScienceOn
24 Clauset, A., Newman, M. and Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70, 66111.   DOI
25 Dinh, T. N. and Thai, M. T. (2013). Towards optimal community detection: From trees to general weighted networks. Internet Mathematics (accepted pending revision).
26 Fawcett, T. (2006). An introduction to ROC analysis. Pattern recognition letters, 27, 861-874.   DOI   ScienceOn
27 Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486, 74-174.
28 Girvan, M. and Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99, 7821-7826.   DOI   ScienceOn
29 Jeong, E. S., Cho, D. Y., Suh, I. W. and Yeo, W. D. (2008). Emerging research field selection of construction & transportation sectors using scientometrics. The Journal of the Korea Contents Association, 8, 231-238.   DOI   ScienceOn
30 Kim, H. (2008). Citation flow of the ASIST proceedings using pathfinder network analysis. Journal of the Korean Society for Information Management, 25, 157-166.   DOI   ScienceOn
31 Kim, J. A. and Lee, H. S. (2008). A study on network analysis for science and technology activity. Proceedings of the Autumn Conference of the Korean Operations Research and Management Science Society, 498-503.
32 Lancichinetti, A. and Fortunato, S. (2009a). Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Physical Review E, 80, 016118.   DOI
33 Lancichinetti, A. and Fortunato, S. (2009b). Community detection algorithms: A comparative analysis. Physical review E, 80, 056117.   DOI