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A classification of the journals in KCI using network clustering methods
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 Title & Authors
A classification of the journals in KCI using network clustering methods
Kim, Jinkwang; Kim, Sohyung; Oh, Changhyuck;
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 Abstract
KCI is a database for the citations of journals and papers published in Korea. Classification of a journal listed in KCI was mainly determined by the publisher who registered the journal at the time of application for the journal. However, journal classification in KCI was known for not properly representing the quoting rate between journals. In this study, we extracted communities of the journals registerd in KCI based on quoting relationship using various network clustering algorithms. Among them, the infomap algorithm turned out to give a classification more being alike to the current KCI's in the aspect of the modular structure.
 Keywords
Community;infomap algorithm;journal classification;KCI;modular;network clustering;
 Language
Korean
 Cited by
 References
1.
Arenas, A., Duch, J., Fernandez, A. and Gomez, S. (2007). Size reduction of complex networks preserving modularity. New Journal of Physics, 9, 176. crossref(new window)

2.
Barnes, E. R. (1982). An algorithm for partitioning the nodes of a graph. SIAM Journal on Algebraic Discrete Methods, 3, 541-550. crossref(new window)

3.
Blondel, V. D., 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.

4.
Carpenter, M. P. and Narin, F. (1973). Clustering of scientific journals. Journal of the American Society for Information Science, 24, 425-436. crossref(new window)

5.
Chun, H., and Leem, B. (2014). Face/non-face channel fit comparison of life insurance company and non-life insurance company using social network analysis. Journal of the Korean Data & Information Science Society, 25, 1207-1219. crossref(new window)

6.
Clauset, A., Newman, M. and Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70, 066111. crossref(new window)

7.
Copic, J., Jackson, M. O. and Kirman, A. (2009). Identifying community structures from network data via maximum likelihood methods, The BE Journal of Theoretical Economics, 9.

8.
Danon, L., Diaz-Guilera, A. and Arenas, A. (2006). The effect of size heterogeneity on community identification in complex networks. Journal of Statistical Mechanics: Theory and Experiment, 2006, P11010.

9.
Donath, W. E. and Hoffman, A. J. (1973). Lower bounds for the partitioning of graphs. IBM Journal of Research and Development, 17, 420-425. crossref(new window)

10.
Fawcett, T. (2006). An introduction to ROC analysis. Pattern recognition letters, 27, 861-874. crossref(new window)

11.
Flake, G. W., Lawrence, S. and Giles, C. L. (2000). Efficient identification of web communities. In Proceedings of the sxth ACM SIGKDD international conference on knowledge discovery and data mining, 150-160, ACM.

12.
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486, 74-174.

13.
Fortunato, S. and Barthelemy, M. (2007). Resolution limit in community detection. Proceedings of the National Academy of Sciences, 104, 36-41. crossref(new window)

14.
Kernighan, B. W. and Lin, S. (1970). An efficient heuristic procedure for partitioning graphs. The Bell System Technical Journal, 49, 291-307. crossref(new window)

15.
Kim, H. (2008). Citation flow of the ASIST proceedings using pathfinder network analysis. Journal of the Korean Society for Information Management, 25, 157-166. crossref(new window)

16.
Kim, J. K., Kim, S. H. and Oh, C. H. (2015). Comparison of journal clustering methods based on citation structure. Journal of the Korean Data & Information Science Society, 26, 827-839. crossref(new window)

17.
Lancichinetti, A. and Fortunato, S. (2009b). Community detection algorithms: A comparative analysis. Physical Review E, 80, 056117. crossref(new window)

18.
Leydesdorff, L. (2004). Clusters and maps of science journals based on bi-connected graphs in the Journal Citation Reports. Journal of Documentation, 60, 371-427. crossref(new window)

19.
Malliaros, F. D. and Vazirgiannis, M. (2013). Clustering and community detection in directed networks: A survey. Physics Reports, 533, 95-142. crossref(new window)

20.
Narin, F., Carpenter, M. and Berlt, N. (1972). Interrelationships of scientific journals. Journal of the American Society for Information Science, 23, 323-331. crossref(new window)

21.
Newman, M. E. and Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69, 026113. crossref(new window)

22.
Newman, M. E. (2004). Detecting community structure in networks. The European Physical Journal BCondensed Matter and Complex Systems, 38, 321-330. crossref(new window)

23.
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. crossref(new window)

24.
O'Malley, A. J. and Marsden, P. V. (2008). The analysis of social networks. Health Services and Outcomes Research Methodology, 8, 222-269. crossref(new window)

25.
Orman, G. K., Labatut, V. and Cherifi, H. (2011). On accuracy of community structure discovery algorithms, arXiv preprint arXiv:1112.4134.

26.
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. crossref(new window)

27.
Rosvall, M. and Bergstrom, C. T. (2008). An information-theoretic framework for resolving community structure in complex networks. Proceedings of the National Academy of Sciences, 104, 7327-7331.

28.
Schaeffer, S. E. (2007). Graph clustering. Computer Science Review, 1, 27-64. crossref(new window)

29.
Scott, J. (2012). Social network analysis, Sage.

30.
Soffer, S. N. and Vazquez, A. (2005). Network clustering coefficient without degree-correlation biases. Physical Review E, 71, 057101. crossref(new window)

31.
Suaris, P. R. and Kedem, G. (1988). An algorithm for quadrisection and its application to standard cell placement. IEEE Transactions on Circuits and Systems, 35, 294-303. crossref(new window)

32.
Tang, L. and Liu, H. (2010). Community detection and mining in social media. Synthesis Lectures on Data Mining and Knowledge Discovery, 2, 1-137.

33.
Wasserman, S. and Faust, K. (1994). Social network analysis: Methods and applications. Cambridge university press, 8.

34.
Watts, D. J. and Strogatz, S. H. (1998). Collective dynamics of small-world networks. Nature, 393, 440-442. crossref(new window)

35.
Won, D., and Choi, K. (2014). Network analysis and comparing citation index of statistics journals. Journal of the Korean Data & Information Science Society, 25, 317-325. crossref(new window)

36.
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. crossref(new window)