Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Overlapping thematic structures extraction with mixed-membership stochastic blockmodel

  • Published:
Scientometrics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Notes

  1. http://www.topic-challenge.info.

  2. https://github.com/premgopalan/svinet.

  3. https://gephi.org/.

  4. https://github.com/ziqizhang/jate.

  5. The clustering result hd is obtained from http://www.topic-challenge.info/solutions.html.

  6. http://scikit-learn.org.

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.

    Article  Google 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.

    MATH  Google 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.

    Article  MATH  Google 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).

  • 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.

    Article  Google Scholar 

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

    MATH  Google 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.

    Article  Google 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.

    Article  MathSciNet  Google 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.

    Article  Google 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.

    Article  MathSciNet  Google 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.

    Article  Google Scholar 

  • Ginsparg, P. (2011). ArXiv at 20. Nature, 476, 145–147. https://doi.org/10.1038/476145a.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  MathSciNet  MATH  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  • Hoffman, M. D., Blei, D. M., Wang, C., & Paisley, J. (2013). Stochastic variational inference. Journal of Machine Learning Research, 14(May), 1303–1347.

    MathSciNet  MATH  Google 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.

    Article  MathSciNet  MATH  Google 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.

    Article  Google 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.

    Article  MATH  Google 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.

    Article  Google 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.

    Article  Google Scholar 

  • Lorenz, M. O. (1905). Methods of measuring the concentration of wealth. Publications of the American Statistical Association, 9(70), 209–219.

    Article  Google Scholar 

  • Manning, C. D., Raghavan, P., & Schütze, H. (Eds.). (2008). Introduction to information retrieval. Cambridge: Cambridge University Press.

    MATH  Google 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.

    Article  Google 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.

    Article  MathSciNet  Google 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).

  • 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.

    MathSciNet  MATH  Google 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.

    Article  Google 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.

  • 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    Article  Google 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.

    MathSciNet  MATH  Google 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.

    Article  MATH  Google 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).

  • 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.

    Article  Google 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.

    Article  Google 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).

  • 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).

  • 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.

    Article  Google 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.

    Article  Google Scholar 

Download references

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).

Author information

Authors and Affiliations

  1. School of Economics and Management, Beijing University of Technology, Beijing, People’s Republic of China

    Shuo Xu, Junwan Liu & Dongsheng Zhai

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, People’s Republic of China

    Shuo Xu

  3. School of Economics and Management, Beijing Forestry University, Beijing, People’s Republic of China

    Xin An

  4. Institute of Scientific and Technical Information of China, Beijing, People’s Republic of China

    Zheng Wang

  5. Library, Shenzhen University, Shenzhen, People’s Republic of China

    Hongshen Pang

Authors
  1. Shuo Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junwan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dongsheng Zhai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xin An
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hongshen Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuo Xu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, S., Liu, J., Zhai, D. et al. Overlapping thematic structures extraction with mixed-membership stochastic blockmodel. Scientometrics 117, 61–84 (2018). https://doi.org/10.1007/s11192-018-2841-4

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-018-2841-4

Keywords

Search

Navigation