Overlapping thematic structures extraction with mixed-membership stochastic blockmodel
- 227 Downloads
Abstract
It is increasing important to identify automatically thematic structures from massive scientific literature. The interdisciplinarity enables thematic structures without natural boundaries. In this work, the identification of thematic structures is regarded as an overlapping community detection problem from the large-scale citation-link network. A mixed-membership stochastic blockmodel, armed with stochastic variational inference algorithm, is utilized to detect the overlapping thematic structures. In the meanwhile, in order to enhance readability, each theme is labeled with soft mutual information based method by several topical terms. Extensive experimental results on the astro dataset indicate that mixed-membership stochastic blockmodel primarily uses the local information and allows for the pervasive overlaps, but it favors similar sized themes, which disqualifies this approach from being used to extract the thematic structures from scientific literature. In addition, the thematic structures from the bibliographic coupling network is similar to those from the co-citation network.
Keywords
Overlapping thematic structure Mixed-membership stochastic blockmodel Stochastic variational inference Soft mutual information Cluster labelingNotes
Acknowledgements
The present study is an extended version of an article (Xu et al. 2017) presented at the 16th International Conference on Scientometrics and Informetrics, Wuhan (China), 16–20 October 2017. The clustering results from this work have been deposited with the other astro-dataset results. Our gratitude also goes to the anonymous reviewers and the editor for their valuable comments. This work was supported partially by the Social Science Foundation of Beijing (Grant No. 17GLB074), Science and Technology Project of Guangdong Province (Grant No. 2017A030303065), and National Natural Science Foundation of China (Grant Nos. 71403255 and 71473237).
References
- Abbe, E. & Sandon, C. (2015). Community detection in general stochastic block models: Fundamental limits and efficient algorithms for recovery. In Proceedings of the 56th IEEE annual symposium on foundations of computer science (pp. 670–688). Washington, DC: IEEE Computer Society. https://doi.org/10.1109/FOCS.2015.47.
- Ahlgren, P., & Colliander, C. (2009). Document–document similarity approaches and science mapping: Experimental comparison of five approaches. Journal of Informetrics, 3(1), 49–63. https://doi.org/10.1016/j.joi.2008.11.003.CrossRefGoogle Scholar
- Airoldi, E. M., Blei, D. M., Fienberg, S. E., & Xing, E. P. (2008). Mixed membership stochastic blockmodels. Journal of Machine Learning Research, 9(Sep), 1981–2014.zbMATHGoogle Scholar
- Amelio, A., & Pizzuti, C. (2014). Overlapping community discovery methods: A survey (pp. 105–125). Vienna: Springer. https://doi.org/10.1007/978-3-7091-1797-2_6.Google Scholar
- An, X., Xu, S., Wen, Y., & Hu, M. (2014). A shared interest discovery model for co-author relationship in SNS. International Journal of Distributed Sensor Networks, 2014, 1–9. https://doi.org/10.1155/2014/820715.Google Scholar
- Ananiadou, S. (1994). A methodology for automatic term recognition. In Proceedings of the 15th international conference on computational linguistics (pp. 1034–1038). Stroudsburg, PA: Association for Computational Linguistics. https://doi.org/10.3115/991250.991317.
- Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003). An introduction to MCMC for machine learning. Machine Learning, 50(1–2), 5–43. https://doi.org/10.1023/A:1020281327116.CrossRefzbMATHGoogle Scholar
- Bastian, M., Heymann, S., and Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. In Proceedings of the 3rd international AAAI conference on weblogs and social media (pp. 361–362).Google Scholar
- Bennett, C. L., Halpern, M., Hinshaw, G., Jarosik, N., Kogut, A., Limon, M., et al. (2003). First-year wilkinson microwave anisotropy probe (WMAP) observations: Preliminary maps and basic results. The Astrophysical Journal Supplement Series, 148(1), 1–27.CrossRefGoogle Scholar
- Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.zbMATHGoogle Scholar
- Boyack, K. W. (2017). Thesaurus-based methods for mapping contents of publication sets. Scientometrics, 111(2), 1141–1155. https://doi.org/10.1007/s11192-017-2304-3.CrossRefGoogle Scholar
- Chen, P.-Y., & Hero, A. O, I. I. I. (2015). Universal phase transition in community detectability under a stochastic block model. Physical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 91(3), 032804. https://doi.org/10.1103/PhysRevE.91.032804.MathSciNetCrossRefGoogle Scholar
- Conroy, C., & Gunn, J. E. (2010). The propagation of uncertainties in stellar population synthesis modeling. III. Model calibration, comparison, and evaluation. The Astrophysical Journal, 712(2), 833–857. https://doi.org/10.1088/0004-637X/712/2/833.CrossRefGoogle Scholar
- Dave, R. N. (1996). Validation fuzzy partition obtained through \(c\)-shells clustering. Pattern Recognition Letters, 17(6), 613–623. https://doi.org/10.1016/0167-8655(96)00026-8.MathSciNetCrossRefGoogle Scholar
- Dhillon, I. S. (2001). Co-clustering documents and words using bipartite spectral graph partitioning. In Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 269–274). New York, NY: ACM. https://doi.org/10.1145/502512.502550.
- Frantzi, K., Ananiadou, S., & Mima, H. (2000). Automatic recognition of multi-word term: The C-value/NC-value method. International Journal on Digital Libraries, 3(2), 115–130. https://doi.org/10.1007/s007999900023.CrossRefGoogle Scholar
- Ginsparg, P. (2011). ArXiv at 20. Nature, 476, 145–147. https://doi.org/10.1038/476145a.CrossRefGoogle Scholar
- Glänzel, W., & Thijs, B. (2011). Using ’core documents’ for the representation of clusters and topics. Scientometrics, 88(1), 297–309. https://doi.org/10.1007/s11192-011-0347-4.CrossRefGoogle Scholar
- Glänzel, W., & Thijs, B. (2017). Using hybrid methods and ’core documents’ for the representation of clusters and topics: The astronomy dataset. Scientometrics, 111(2), 1071–1087. https://doi.org/10.1007/s11192-017-2301-6.CrossRefGoogle Scholar
- Gläser, J., Glänzel, W., & Scharnhorst, A. (2017). Same data-different results? Towards a comparative approach to the identification of thematic structures in science. Scientometrics, 111(2), 981–998. https://doi.org/10.1007/s11192-017-2296-z.CrossRefGoogle Scholar
- Gopalan, P. K., & Blei, D. M. (2013). Efficient discovery of overlapping communities in massive networks. Proceedings of the National Academy of Sciences of the United States of America, 110(36), 14534–14539. https://doi.org/10.1073/pnas.1221839110.MathSciNetCrossRefzbMATHGoogle Scholar
- Goswami, S., Murthy, C. A., and Das, A. K. (2016). Sparsity measure of a network graph: Gini index. eprint arXiv:1612.07074.
- Havemann, F., Gläser, J., & Heinz, M. (2017). Memetic search for overlapping topics based on a local evaluation of link communities. Scientometrics, 111(2), 1089–1118. https://doi.org/10.1007/s11192-017-2302-5.CrossRefGoogle Scholar
- Havemann, F., Gläser, J., Heinz, M., & Struck, A. (2012). Identifying overlapping and hierarchical thematic structures in networks of scholarly papers: A comparison of three approaches. PLoS ONE, 7(3), e33255. https://doi.org/10.1371/journal.pone.0033255.CrossRefGoogle Scholar
- Healey, P., Rothman, H., & Hoch, P. K. (1986). An experiment in science mapping for research planning. Research Policy, 15(5), 233–251. https://doi.org/10.1016/0048-7333(86)90024-7.CrossRefGoogle Scholar
- Hoffman, M. D., Blei, D. M., Wang, C., & Paisley, J. (2013). Stochastic variational inference. Journal of Machine Learning Research, 14(May), 1303–1347.MathSciNetzbMATHGoogle Scholar
- Hurley, N., & Rickard, S. (2009). Comparing measures of sparsity. IEEE Transactions on Information Theory, 55(10), 4723–4741. https://doi.org/10.1109/TIT.2009.2027527.MathSciNetCrossRefzbMATHGoogle Scholar
- Janssens, F., Glänzel, W., & de Moor, B. (2008). A hybrid mapping of information science. Scientometrics, 75(3), 607–631. https://doi.org/10.1007/s11192-007-2002-7.CrossRefGoogle Scholar
- Jordan, M., Grhahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183–233. https://doi.org/10.1023/A:1007665907178.CrossRefzbMATHGoogle Scholar
- Klavans, R., & Boyack, K. W. (2011). Using global mapping to create more accurate document-level maps of research fields. Journal of the Association for Information Science and Technology, 62(1), 1–18. https://doi.org/10.1002/asi.21444.Google Scholar
- Koopman, R., & Wang, S. (2017). Mutual information based labelling and comparing clusters. Scientometrics, 111(2), 1157–1167. https://doi.org/10.1007/s11192-017-2305-2.CrossRefGoogle Scholar
- Leydesdorff, L., & Welbers, K. (2011). The semantic mapping of words and co-words in contexts. Journal of Informetrics, 5(3), 469–475. https://doi.org/10.1016/j.joi.2011.01.008.CrossRefGoogle Scholar
- Lorenz, M. O. (1905). Methods of measuring the concentration of wealth. Publications of the American Statistical Association, 9(70), 209–219.CrossRefGoogle Scholar
- Manning, C. D., Raghavan, P., & Schütze, H. (Eds.). (2008). Introduction to information retrieval. Cambridge: Cambridge University Press.zbMATHGoogle Scholar
- Matsuo, Y., & Ishizuka, M. (2004). Keyword extraction from a single document using word co-occurrence statistical information. International Journal on Artificial Intelligence Tools, 13(01), 157–169. https://doi.org/10.1142/S0218213004001466.CrossRefGoogle Scholar
- Mei, Q., Shen, X., and Zhai, C. (2007). Automatic labeling of multinomial topic models. In Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 490–499). https://doi.org/10.1145/1281192.1281246.
- Nepusz, T., Petróczi, A., Négyessy, L., & Bazsó, F. (2008). Fuzzy communities and the concept of bridgeness in complex networks. Physical Review E, 77(1), 016107. https://doi.org/10.1103/PhysRevE.77.016107.MathSciNetCrossRefGoogle Scholar
- Park, Y., Byrd, R. J., and Boguraev, B. K. (2002). Automatic glossary extraction: Beyond terminology identification. In Proceedings of the 19th international conference on computational linguistics, Taipei, Taiwan (pp. 1–7).Google Scholar
- Pedregosa, F., Varoquaus, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12(Oct), 2825–2830.MathSciNetzbMATHGoogle Scholar
- Role, F., & Nadif, M. (2014). Beyond cluster labeling: Semantic interpretation of clusters’ contents using a graph representation. Knowledge-based System, 56, 141–155. https://doi.org/10.1016/j.knosys.2013.11.005.CrossRefGoogle Scholar
- Rose, S., Engel, D., Cramer, N., & Cowley, W. (2010). In M. W. Berry & J. Kogan (Eds.), Text mining: Application and theory (pp. 1–20). Hoboken: Wiley.Google Scholar
- Sclano, F. and Velardi, P. (2007). Termextractor: A web application to learn the common terminology of interest groups and research communities. In Proceedings of the 3rd international conference on interoperability for enterprise software and applications.Google Scholar
- Shi, Q., Qiao, X., Xu, S., & Nong, G. (2013). Author-topic evolution model and its application in analysis of research interests evolution. Journal of the China Society for Scientific and Technical Information, 32(9), 912–919.Google Scholar
- Shibata, N., Kajikawa, Y., Takeda, Y., & Matsushima, K. (2009). Comparative study on methods of detecting research fronts using different types of citation. Journal of the Association for Information Science and Technology, 60(3), 571–580. https://doi.org/10.1002/asi.20994.Google Scholar
- Skrutskie, M. F., Cutri, R. M., Stiening, R., Weinberg, M. D., Schneider, S., Carpenter, J. M., et al. (2006). The two micron all sky survey (2MASS). The Astronomical Journal, 131(2), 1163–1183.CrossRefGoogle Scholar
- van Eck, N. J., & Waltman, L. (2009). How to normalize cooccurrence data? an analysis of some well-known similarity measures. Journal of the Association for Information Science and Technology, 60(8), 1635–1651. https://doi.org/10.1002/asi.21075.Google Scholar
- van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3.CrossRefGoogle Scholar
- van Eck, N. J., & Waltman, L. (2017). Citation-based clustering of publications using CitNetExplorer and VOSviewer. Scientometrics, 111(2), 1053–1070. https://doi.org/10.1007/s11192-017-2300-7.CrossRefGoogle Scholar
- van Raan, A. F. J. (1996). Advanced bibliometric methods as quantitative core of peer review based evaluation and foresight exercises. Scientometrics, 36(3), 397–420. https://doi.org/10.1007/BF02129602.CrossRefGoogle Scholar
- Velden, T., Boyack, K. W., Gläser, J., Koopman, R., Scharnhorst, A., & Wang, S. (2017). Comparison of topic extraction approaches and their results. Scientometrics, 111(2), 1169–1221. https://doi.org/10.1007/s11192-017-2306-1.CrossRefGoogle Scholar
- Vinh, N. X., Epps, J., & Bailey, J. (2010). Information theoretic measures for clustering comparison: Variants, properties, normalization and correction for chance. Journal of Machine Learning Research, 11(Oct), 2837–2854.MathSciNetzbMATHGoogle Scholar
- Waltman, L., & van Eck, N. J. (2012). A new methodology for constructing a publication-level classification system of science. Journal of the Association for Information Science and Technology, 63(12), 2378–2392. https://doi.org/10.1002/asi.22748.Google Scholar
- Wilk, M. B., & Gnanadesikan, R. (1968). Probability plotting methods for the analysis for the analysis of data. Biometrika, 55(1), 1–17. https://doi.org/10.1093/biomet/55.1.1.Google Scholar
- Xie, J., Kelley, S., & Szymanski, B. K. (2013). Overlapping community detection in networks: The state-of-the-art and comparative study. ACM Computing Surveys, 45(4), 43:1–43:35. https://doi.org/10.1145/2501654.2501657.CrossRefzbMATHGoogle Scholar
- Xu, S., Liu, J., & Wang, Z. (2017). Overlapping thematic structures extraction with mixed-membership stochastic blockmodel. In Proceedings of ISSI 2017—the 16th international conference on scientometrics & informetrics (pp. 1007–1012).Google Scholar
- Xu, S., Qiao, X., Zhu, L., Zhang, Y., Xue, C., & Li, L. (2016). Reviews on determining the number of clusters. Applied Mathematics & Information Sciences, 10(4), 1493–1512.CrossRefGoogle Scholar
- Xu, S., Shi, Q., Qiao, X., Zhu, L., Zhang, H., Jung, H., et al. (2014). A dynamic users’ interest discovery model with distributed inference algorithm. International Journal of Distributed Sensor Networks, 2014, 1–11. https://doi.org/10.1155/2014/280892.Google Scholar
- Yau, C.-K., Porter, A., Newman, N., & Suominen, A. (2014). Clustering scientific documents with topic modeling. Scientometrics, 100(3), 767–786. https://doi.org/10.1007/s11192-014-1321-8.CrossRefGoogle Scholar
- Zhang, Z., Gao, J., & Ciravegna, F. (2016). JATE 2.0: Java automatic term extraction with Apache Solr. In Proceedings of the 10th language resources and evaluation conference (pp. 2262–2269).Google Scholar
- Zhang, Z., Iria, J., Brewster, C., & Ciravegna, F. (2008). A comparative evaluation of term recognition algorithms. In Proceedings of the 6th international conference on language resources and evaluation, Marrakech, Morocco (pp. 2108–2113).Google Scholar
- Zhu, G., Blanton, M. R., & Moustakas, J. (2010). Stellar populations of elliptical galaxies in the local universe. The Astrophysical Journal, 722(1), 491–519. https://doi.org/10.1088/0004-637X/722/1/491.CrossRefGoogle Scholar
- Zitt, M., Ramanana-Rahary, S., & Bassecoulard, E. (2005). Relativity of citation performance and excellence measures: From cross-field to cross-scale effects of field-normalisation. Scientometrics, 63(2), 373–401. https://doi.org/10.1007/s11192-005-0218-y.CrossRefGoogle Scholar