Identifying collaboration dynamics of bipartite author-topic networks with the influences of interest changes

  • 44 Accesses


Knowing driving factors and understanding researcher behaviors from the dynamics of collaborations over time offer some insights, i.e. help funding agencies in designing research grant policies. We present longitudinal network analysis on the observed collaborations through co-authorship over 15 years. Since co-authors possibly influence researchers to have interest changes, by focusing on researchers who could become the influencer, we propose a stochastic actor-oriented model of bipartite (two-mode) author-topic networks from article metadata. Information of scientific fields or topics of article contents, which could represent the interests of researchers, are often unavailable in the metadata. Topic absence issue differentiates this work with other studies on collaboration dynamics from article metadata of title-abstract and author properties. Therefore, our works also include procedures to extract and map clustered keywords as topic substitution of research interests. Then, the next step is to generate panel-waves of co-author networks and bipartite author-topic networks for the longitudinal analysis. The proposed model is used to find the driving factors of co-authoring collaboration with the focus on researcher behaviors in interest changes. This paper investigates the dynamics in an academic social network setting using selected metadata of publicly-available crawled articles in interrelated domains of “natural language processing” and “information extraction”. Based on the evidence of network evolution, researchers have a conformed tendency to co-author behaviors in publishing articles and exploring topics. Our results indicate the processes of selection and influence in forming co-author ties contribute some levels of social pressure to researchers. Our findings also discussed on how the co-author pressure accelerates the changes of interests and behaviors of the researchers.

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

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12


  1. Abbasi, A., Hossain, L., Uddin, S., & Rasmussen, K. J. R. (2011). Evolutionary dynamics of scientific collaboration networks: Multi-levels and cross-time analysis. Scientometrics,89(2), 687.

  2. Abrahams, B., Sitas, N., & Esler, K. J. (2019). Exploring the dynamics of research collaborations by mapping social networks in invasion science. Journal of Environmental Management,229, 27–37.

  3. Amjad, T., Daud, A., & Song, M. (2018). Measuring the impact of topic drift in scholarly networks. In Companion Proceedings of the The Web Conference 2018 (pp. 373–378). Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee.

  4. Aubaidan, B., Mohd, M., & Albared, M. (2014). Comparative study of K-means and K-Means ++ clustering algorithms on crime domain. Journal of Computer Science,10(7), 1197–1206.

  5. Ayaz, S., Masood, N., & Islam, M. A. (2018). Predicting scientific impact based on h-index. Scientometrics,114(3), 993–1010.

  6. Beel, J., Gipp, B., Langer, S., & Breitinger, C. (2016). Research-paper recommender systems: A literature survey. International Journal on Digital Libraries,17(4), 305–338.

  7. Bozeman, B., & Corley, E. (2004). Scientists’ collaboration strategies: implications for scientific and technical human capital. Research Policy,33(4), 599–616.

  8. Datta, S., Basuchowdhuri, P., Acharya, S., & Majumder, S. (2017). The habits of highly effective researchers: An empirical study. IEEE Transactions on Big Data,3(1), 3–17.

  9. Daud, A., Li, J., Zhou, L., & Muhammad, F. (2010). Temporal expert finding through generalized time topic modeling. Knowledge-Based Systems,23(6), 615–625.

  10. de Siqueira, G. O., Canuto, S., Gonçalves, M. A., & Laender, A. H. F. (2018). A pragmatic approach to hierarchical categorization of research expertise in the presence of scarce information. International Journal on Digital Libraries.

  11. Deng, H., Han, J., Lyu, M. R., & King, I. (2012). Modeling and exploiting heterogeneous bibliographic networks for expertise ranking. In Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (pp. 71–80).

  12. Ebadi, A., & Schiffauerova, A. (2015). On the relation between the small world structure and scientific activities. PLoS ONE,10(3), e0121129.

  13. Ferligoj, A., Kronegger, L., Mali, F., Snijders, T. A. B., & Doreian, P. (2015). Scientific collaboration dynamics in a national scientific system. Scientometrics,104(3), 985–1012.

  14. Fu, T. Z. J., Song, Q., & Chiu, D. M. (2014). The academic social network. Scientometrics,101(1), 203–239.

  15. Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences,101(Supplement 1), 5228–5235.

  16. Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences,102(46), 16569–16572.

  17. Hornik, K., Feinerer, I., Kober, M., & Buchta, C. (2012). Spherical k-means clustering. Journal of Statistical Software Articles,50(10), 1–22.

  18. Hou, H., Wang, C., Luan, C., Wang, X., & Zhuang, P. (2013). The dynamics of scientific collaboration networks in scientometrics. COLLNET Journal of Scientometrics and Information Management,7(1), 121–140.

  19. Iefremova, O., Wais, K., & Kozak, M. (2018). Biographical articles in scientific literature: Analysis of articles indexed in Web of Science. Scientometrics,117(3), 1695–1719.

  20. Iglič, H., Doreian, P., Kronegger, L., & Ferligoj, A. (2017). With whom do researchers collaborate and why? Scientometrics,112(1), 153–174.

  21. Jung, J. J. (2015). Big bibliographic data analytics by random walk model. Mobile Networks and Applications,20(4), 533–537.

  22. Kong, X., Jiang, H., Wang, W., Bekele, T. M., Xu, Z., & Wang, M. (2017). Exploring dynamic research interest and academic influence for scientific collaborator recommendation. Scientometrics,113(1), 369–385.

  23. Kong, X., Shi, Y., Yu, S., Liu, J., & Xia, F. (2019). Academic social networks: Modeling, analysis, mining and applications. Journal of Network and Computer Applications,132, 86–103.

  24. Kosmulski, M. (2012). The order in the lists of authors in multi-author papers revisited. Journal of Informetrics,6(4), 639–644.

  25. Li, H., An, H., Wang, Y., Huang, J., & Gao, X. (2016). Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network. Physica A: Statistical Mechanics and its Applications,450, 657–669.

  26. Liang, W., Jin, Q., Lu, Z., Wu, M., & Hu, C. (2016). Analyzing of research patterns based on a temporal tracking and assessing model. Personal and Ubiquitous Computing,20(6), 933–946.

  27. Lin, S., Hong, W., Wang, D., & Li, T. (2017). A survey on expert finding techniques. Journal of Intelligent Information Systems,49(2), 255–279.

  28. Manger, M. S., Pickup, M. A., & Snijders, T. A. B. (2012). A hierarchy of preferences: A longitudinal network analysis approach to PTA formation. Journal of Conflict Resolution,56(5), 853–878.

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

  30. Meho, L. I. (2019). Using Scopus’s CiteScore for assessing the quality of computer science conferences. Journal of Informetrics,13(1), 419–433.

  31. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th international conference on neural information processing systemsVolume 2 (pp. 3111–3119).

  32. Oliveira, M., Curado, C., & Henriques, P. L. (2018). Knowledge sharing among scientists: A causal configuration analysis. Journal of Business Research.

  33. Ortega, J. L. (2014). AMiner: Science networking as an information source. In J. L. Ortega (Ed.), Academic search engines (pp. 47–70). Oxford: Chandos Publishing.

  34. Purwitasari, D., Fatichah, C., Arieshanti, I., & Hayatin, N. (2016). K-medoids algorithm on Indonesian Twitter feeds for clustering trending issue as important terms in news summarization. In Proceedings of 2015 international conference on information and communication technology and systems, ICTS 2015 (pp. 95–98).

  35. Purwitasari, D., Fatichah, C., Purnama, I. K. E., Sumpeno, S., & Purnomo, M. H. (2017). Inter-departmental research collaboration recommender system based on content filtering in a cold start problem. In 2017 IEEE 10th international workshop on computational intelligence and applications, IWCIA 2017proceedings (Vol. 2017-Decem).

  36. Purwitasari, D., Fatichah, C., Sumpeno, S., & Purnomo, M. H. (2018a). Ekstraksi Ciri Produktivitas Dinamis untuk Prediksi Topik Pakar dengan Model Discrete Choice. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 7(4), 418–426.

  37. Purwitasari, D., Ilmi, A. B., Fatichah, C., Fauzi, W. A., Sumpeno, S., & Purnomo, M. H. (2018b). Conflict of interest based features for expert classification in bibliographic network. In 2018 IEEE international conference on computer engineering, network and intelligent multimedia, CENIM 2018proceedings.

  38. Purwitasari, D., Priantara, I. W. S., Kusmawan, P. Y., Yuhana, U. L., & Siahaan, D. O. (2011). The use of Hartigan index for initializing K-means ++ in detecting similar texts of clustered documents as a plagiarism indicator. Asian Journal of Information Technology,10(8), 341–347.

  39. Renoust, B., Melançon, G., & Viaud, M.-L. (2014). Entanglement in multiplex networks: Understanding group cohesion in homophily networks. In R. Missaoui & I. Sarr (Eds.), Social network analysis—Community detection and evolution (pp. 89–117). Cham: Springer.

  40. Röder, M., Both, A., & Hinneburg, A. (2015). Exploring the space of topic coherence measures. In Proceedings of the eighth ACM international conference on web search and data mining (pp. 399–408). New York, NY, USA: ACM.

  41. Shibayama, S. (2019). Sustainable development of science and scientists: Academic training in life science labs. Research Policy,48(3), 676–692.

  42. Siciliano, M. D., Welch, E. W., & Feeney, M. K. (2018). Network exploration and exploitation: Professional network churn and scientific production. Social Networks,52, 167–179.

  43. Snijders, T. A. B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology,31(1), 361–395.

  44. Snijders, T. A. B., Lomi, A., & Torló, V. J. (2013). A model for the multiplex dynamics of two-mode and one-mode networks, with an application to employment preference, friendship, and advice. Social Networks,35(2), 265–276.

  45. Snijders, T. A. B., van de Bunt, G. G., & Steglich, C. E. G. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks,32(1), 44–60.

  46. Steglich, C., Snijders, T. A. B., & Pearson, M. (2010). Dynamic networks and behavior: Separating selection from influence. Sociological Methodology,40(1), 329–393.

  47. Suominen, A., & Toivanen, H. (2016). Map of science with topic modeling: Comparison of unsupervised learning and human-assigned subject classification. Journal of the Association for Information Science and Technology,67(10), 2464–2476.

  48. Tang, J. (2016). AMiner: Toward understanding big scholar data. In Proceedings of the ninth ACM international conference on web search and data mining (p. 467). New York, NY, USA: ACM.

  49. Tang, J., Yao, L., Zhang, D., & Zhang, J. (2010). A combination approach to web user profiling. ACM Transactions on Knowledge Discovery from Data,5(1), 2:1–2:44.

  50. Tang, J., Zhang, D., & Yao, L. (2007). Social network extraction of academic researchers. In Proceedings of the 2007 seventh IEEE international conference on data mining (pp. 292–301). Washington, DC, USA: IEEE Computer Society.

  51. Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008). ArnetMiner: Extraction and mining of academic social networks. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 990–998). New York, NY, USA: ACM.

  52. Wang, B., Bu, Y., & Huang, W. (2018). Document- and keyword-based author co-citation analysis. Data and Information Management,2(2), 70–82.

  53. Wen, L., & Junping, Q. (2014). Semantic information retrieval research based on co-occurrence analysis. Online Information Review,38(1), 4–23.

  54. Xia, F., Wang, W., Bekele, T. M., & Liu, H. (2017). Big scholarly data: A survey. IEEE Transactions on Big Data,3(1), 18–35.

  55. Zhu, J., Zhang, J., Zhang, C., Wu, Q., Jia, Y., Zhou, B., et al. (2017). CHRS: Cold start recommendation across multiple heterogeneous information networks. IEEE Access,5, 15283–15299.

Download references


This work as parts of a dissertation about scholar profiles in expert recommendation system was funded by the Indonesia Endowment Fund for Education (LPDP in Indonesian) with the grant number PRJ-4228/LPDP.3/2016 of the LPDP Doctoral Scholarship Programme fiscal year 2017–2020. Some sections of the manuscript was prepared during September-December 2018 in University of Groningen, the Netherlands under Enhancing International Publication (EIP or PKPI in Indonesian) Program by Ministry of Research, Technology and Higher Education of the Republic of Indonesia (RISTEKDIKTI in Indonesian). Furthermore, this research was also partially funded by RISTEKDIKTI under World Class Universities (WCU) Program managed by Institut Teknologi Bandung, Indonesia in 2019.

Author information

Correspondence to Diana Purwitasari.

Ethics declarations

Conflict of interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Purwitasari, D., Fatichah, C., Sumpeno, S. et al. Identifying collaboration dynamics of bipartite author-topic networks with the influences of interest changes. Scientometrics (2020) doi:10.1007/s11192-019-03342-2

Download citation


  • Longitudinal network analysis
  • Scientific collaboration dynamics
  • Research interest changes
  • One mode co-author network
  • Bipartite (two-mode) author-topic network
  • Stochastic actor-oriented model

Mathematics Subject Classification

  • 68T30
  • 68U15
  • 90B15
  • 91B16
  • 91C20
  • 91D30

JEL Classification

  • C31
  • C38
  • C44
  • D80
  • D85