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Exploratory Study of Developing a Synchronization-Based Approach for Multi-step Discovery of Knowledge Structures
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 Title & Authors
Exploratory Study of Developing a Synchronization-Based Approach for Multi-step Discovery of Knowledge Structures
Yu, So Young;
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As Topic Modeling has been applied in increasingly various domains, the difficulty in naming and characterizing topics also has been recognized more. This study, therefore, explores an approach of combining text mining with network analysis in a multi-step approach. The concept of synchronization was applied to re-assign the top author keywords in more than one topic category, in order to improve the visibility of the topic-author keyword network, and to increase the topical cohesion in each topic. The suggested approach was applied using 16,548 articles with 2,881 unique author keywords in construction and building engineering indexed by KSCI. As a result, it was revealed that the combined approach could improve both the visibility of the topic-author keyword map and topical cohesion in most of the detected topic categories. There should be more cases of applying the approach in various domains for generalization and advancement of the approach. Also, more sophisticated evaluation methods should also be necessary to develop the suggested approach.
Synchronization;Ego-centric Network;Topic Modeling;Informetrics;
 Cited by
자아 중심 네트워크 분석과 동적 인용 네트워크를 활용한 토픽모델링 기반 연구동향 분석에 관한 연구,유소영;

정보관리학회지, 2015. vol.32. 1, pp.153-169 crossref(new window)
Combining Ego-centric Network Analysis and Dynamic Citation Network Analysis to Topic Modeling for Characterizing Research Trends, Journal of the Korean Society for information Management, 2015, 32, 1, 153  crossref(new windwow)
Arenas, A., Diaz-Guilera, A., Kurths, J., Moreno, Y., & Zhou, C. (2008). Synchronization in complex networks. Physics Reports, 469(3), 93-153. crossref(new window)

Asuncion, A., Welling, M., Smyth, P., & Teh, Y. W. (2009, June). On smoothing and inference for topic models. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (pp. 27-34). AUAI Press.

Brown, P. F., Pietra, V. J. D., Mercer, R. L., Pietra, S. A. D., & Lai, J. C. (1992). An estimate of an upper bound for the entropy of English. Computational Linguistics, 18(1), 31-40.

Blasius, B., Huppert, A., & Stone, L. (1999). Complex dynamics and phase synchronization in spatially extended ecological systems. Nature, 399(6734), 354-359. crossref(new window)

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.

Blei, D. M., & McAuliffe, J. D. (2010). Supervised topic models. arXiv preprint arXiv:1003.0783.

Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J. L., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. In Advances in neural information processing systems (pp. 288-296).

Chuang, J., Ramage, D., Manning, C., & Heer, J. (2012). Interpretation and trust: Designing model-driven visualizations for text analysis. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 443-452). ACM.

Elowitz, M. B., & Leibler, S. (2000). A synthetic oscillatory network of transcriptional regulators. Nature, 403(6767), 335-338. crossref(new window)

Garcia-Ojalvo, J., Elowitz, M. B., & Strogatz, S. H. (2004). Modeling a synthetic multicellular clock: Repressilators coupled by quorum sensing. Proceedings of the National Academy of Sciences of the United States of America, 101(30), 10955-10960. crossref(new window)

Jalili, M. (2013). Enhancing synchronizability of diffusively coupled dynamical networks: A survey. Neural Networks and Learning Systems, IEEE Transactions on, 24(7), 1009-1022. crossref(new window)

Jha, S. S., & Yadava, R. D. S. (2012). Synchronization based saw sensor using delay line coupled dual oscillator phase dynamics. Sensors & Transducers (1726-5479), 141(6), 71-91.

Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1), 5228-5235. crossref(new window)

Hall, D., Jurafsky, D., & Manning, C. D. (2008). Studying the history of ideas using topic models. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 363-371). Association for Computational Linguistics.

Kang, B-I., Song, M., Jho, H.S. (2013). A study on opinion mining of newspaper texts based on topic modeling. Journal of the Korean Library and Information Science Society, 47(4), 315-334. crossref(new window)

Kuramoto, Y., & Nishikawa, I. (1987). Statistical macrodynamics of large dynamical systems. Case of a phase transition in oscillator communities. Journal of Statistical Physics, 49(3-4), 569-605. crossref(new window)

Lu, Y., & Zhai, C. (2008). Opinion integration through semi-supervised topic modeling. In Proceedings of the 17th international conference on World Wide Web (pp. 121-130). ACM.

Mirollo, R. E., & Strogatz, S. H. (1990). Synchronization of pulse-coupled biological oscillators. SIAM Journal on Applied Mathematics, 50(6), 1645-1662. crossref(new window)

Miyano, T., & Tsutsui, T. (2007a). Data synchronization in a network of coupled phase oscillators. Physical Review Letters, 98(2), 024102. crossref(new window)

Miyano, T., & Tsutsui, T. (2007b). Extracting feature patterns in the health status of elderly people needing nursing care by data synchronization. In Information Technology Applications in Biomedicine, 2007. ITAB 2007. 6th International Special Topic Conference on (pp. 153-156). IEEE.

Miyano, T., & Tsutsui, T. (2008a). Collective synchronization as a method of learning and generalization from sparse data. Physical Review E, 77(2), 026112. crossref(new window)

Miyano, T., & Tsutsui, T. (2008b). Finding major patterns of aging process by data synchronization. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 91(9), 2514-2519.

Miyano, T., & Tsutsui, T. (2009). Link of data synchronization to self-organizing map algorithm. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 92(1), 263-269.

Miyano, T., & Tatsumi, K. (2012). Determining anomalous dynamic patterns in price indexes of the London Metal Exchange by data synchronization. Physica A: Statistical Mechanics and its Applications, 391(22), 5500-5511. crossref(new window)

Miyano, T., & Tsutsui, T. (2007). Data synchronization as a method of data mining. In Proceedings of the 2007 International Symposium on Nonlinear Theory and its Applications NOLTA'07 (pp 224-227). Vancouver: NOLTA.

Niebur, E., Schuster, H. G., Kammen, D. M., & Koch, C. (1991). Oscillator-phase coupling for different two-dimensional network connectivities. Physical Review A, 44(10), 6895. crossref(new window)

Park, J-H. & Song, M. (2013). A study on the research trends in library & information science in Korea using topic modeling. Journal of Korean Society for Information Management, 30(1), 7-32. crossref(new window)

Pikovsky, A., Rosenblum, M., & Kurths, J. (Eds.). (2003). Synchronization: A universal concept in nonlinear sciences (Vol. 12). London: Cambridge University Press.

Pluchino, A., Latora, V., & Rapisarda, A. (2005). Changing opinions in a changing world: A new perspective in sociophysics. International Journal of Modern Physics C, 16(04), 515-531. crossref(new window)

Ramage, D., Hall, D., Nallapati, R., & Manning, C. D. (2009a). Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 (pp. 248-256). Association for Computational Linguistics.

Ramage, D., Rosen, E., Chuang, J., Manning, C. D., & McFarland, D. A. (2009b). Topic modeling for the social sciences. In NIPS 2009 Workshop on Applications for Topic Models: Text and Beyond (Vol. 5).

Ramage, D., Manning, C. D., & Dumais, S. (2011). Partially labeled topic models for interpretable text mining. In Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 457-465). ACM.

Strogatz, S. H., & Mirollo, R. E. (1988). Collective synchronisation in lattices of nonlinear oscillators with randomness. Journal of Physics A: Mathematical and General, 21(13), L699. crossref(new window)

Strogatz, S. H. (2000). From Kuramoto to Crawford: Exploring the onset of synchronization in populations of coupled oscillators. Physica D: Nonlinear Phenomena, 143(1), 1-20. crossref(new window)

Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268-276. crossref(new window)

Strogatz, S. (2003). Sync: The emerging science of spontaneous order. New York: Hyperion.

Talley, E. M., Newman, D., Mimno, D., Herr II, B. W., Wallach, H. M., Burns, G. A. P. C., Leenders, A. G. M., & McCallum, A. (2011). Database of NIH grants using machine-learned categories and graphical clustering. Nature Methods, 8(6), 443-444. crossref(new window)

Tang, J., Wang, B., Yang, Y., Hu, P., Zhao, Y., Yan, X., & Usadi, A. K. (2012). Patentminer: Topic-driven patent analysis and mining. In Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1366-1374). ACM.

Tilles, P. F., Cerdeira, H. A., & Ferreira, F. F. (2013). Local attractors, degeneracy and analyticity: Symmetry effects on the locally coupled Kuramoto model. Chaos, Solitons & Fractals, 49, 32-46. crossref(new window)

Titov, I., & McDonald, R. (2008). Modeling online reviews with multi-grain topic models. In Proceedings of the 17th international conference on World Wide Web (pp. 111-120). ACM.

Wan, M., Li, L., Xiao, J., Yang, Y., Wang, C., & Guo, X. (2010). CAS based clustering algorithm for Web users. Nonlinear Dynamics, 61(3), 347-361. crossref(new window)

Yu, S.Y. (2013). Applying TDP (Topic Descriptor Profile) with article-level citation flow for analyzing research trend, In proceedings of the 2013 Korean Society for Information Management Conference in Autumn (pp. 39-58). Seoul: Korean Society for Information Management.