Skip to main content
Log in

Exploring dynamic research interest and academic influence for scientific collaborator recommendation

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

In many cases, it is time-consuming for researchers to find proper collaborators who can provide researching guidance besides simply collaborating. The Most Beneficial Collaborators (MBCs), who have high academic level and relevant research topics, can genuinely help researchers to enrich their research. However, how can we find the MBCs? In this paper, we propose a most Beneficial Collaborator Recommendation model called BCR. BCR learns on researchers’ publications and associates three academic features: topic distribution of research interest, interest variation with time and researchers’ impact in collaborators network. We run a topic model on researchers’ publications in each year for topic clustering. The generated topic distribution matrix is fixed by a time function to fit the interest dynamic transformation. The academic social impact is also considered to mine the most prolific researchers. Finally, a TopN MBCs recommendation list is generated according to the computed score. Extensive experiments on a dataset with citation network demonstrate that, in comparison to relevant baseline approaches, our BCR performs better in terms of precision, recall, F1 score and the recommendation quality.

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

Access this article

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

Similar content being viewed by others

References

  • Abramo, G., D’Angelo, C. A., & Di Costa, F. (2009). Research collaboration and productivity: Is there correlation? Higher Education, 57(2), 155–171.

    Article  Google Scholar 

  • Afra, S., Aksaç, A., Õzyer, T., & Alhajj, R. (2017). Link prediction by network analysis. In Prediction and inference from social networks and social media (pp. 97–114). Springer.

  • Bai, X., Hou, J., Du, H., Kong, X., & Xia, F. (2017). Evaluating the impact of articles with geographical distances between institutions. In Proceedings of the 26th international conference on world wide web companion (pp. 1243–1244). International World Wide Web Conferences Steering Committee.

  • Balog, K., Azzopardi, L., & De Rijke, M. (2006). Formal models for expert finding in enterprise corpora. In Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval (pp. 43–50). ACM.

  • Benchettara, N., Kanawati, R., & Rouveirol, C. (2010). A supervised machine learning link prediction approach for academic collaboration recommendation. In Proceedings of the fourth ACM conference on recommender systems (pp. 253–256). ACM.

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

    MATH  Google Scholar 

  • Brandão, M. A., & Moro, M. M. (2016). Social professional networks: A survey and taxonomy. Computer Communications. doi:10.1016/j.comcom.2016.12.011.

  • Canavero, F., Franceschini, F., Maisano, D., & Mastrogiacomo, L. (2014). Impact of journals and academic reputations of authors: A structured bibliometric survey of the IEEE publication galaxy. IEEE Transactions on Professional Communication, 57(1), 17–40.

    Article  Google Scholar 

  • Chen, H. H., Gou, L., Zhang, X., & Giles, C. L. (2011). Collabseer: A search engine for collaboration discovery. In Proceedings of the 11th annual international ACM/IEEE joint conference on digital libraries (pp. 231–240). ACM.

  • Daud, A. (2012). Using time topic modeling for semantics-based dynamic research interest finding. Knowledge-Based Systems, 26, 154–163.

    Article  Google Scholar 

  • Davis, P., & Fromerth, M. (2007). Does the arxiv lead to higher citations and reduced publisher downloads for mathematics articles? Scientometrics, 71(2), 203–215.

    Article  Google Scholar 

  • Desrosiers, C., & Karypis, G. (2011). A comprehensive survey of neighborhood-based recommendation methods. In F. Ricci, L. Rokach, B. Shapira, & P. Kantor (Eds.), Recommender systems handbook (pp. 107–144). Berlin: Springer.

    Chapter  Google Scholar 

  • Dong, Y., Tang, J., Wu, S., Tian, J., Chawla, N. V., Rao, J., & Cao, H. (2012). Link prediction and recommendation across heterogeneous social networks. In Data mining (ICDM), 2012 IEEE 12th international conference on (pp. 181–190). IEEE.

  • George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321–326.

    Article  Google Scholar 

  • Giles, C. L., Bollacker, K. D., & Lawrence, S. (1998). Citeseer: An automatic citation indexing system. In Proceedings of the third ACM conference on digital libraries (pp. 89–98). ACM.

  • Gupta, D., & Berberich, K. (2014). Identifying time intervals of interest to queries. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management (pp. 1835–1838). ACM.

  • Hernando, A., Villuendas, D., Vesperinas, C., Abad, M., & Plastino, A. (2010). Unravelling the size distribution of social groups with information theory in complex networks. The European Physical Journal B, 76(1), 87–97.

    Article  MATH  Google Scholar 

  • Kanhabua, N., & Nørvåg, K. (2010). Determining time of queries for re-ranking search results. In ECDL (Vol. 10, pp. 261–272).

  • Katz, J. S., & Martin, B. R. (1997). What is research collaboration?. Research policy, 26(1), 1–18.

    Article  Google Scholar 

  • Kong, X., Jiang, H., Yang, Z., Xu, Z., Xia, F., & Tolba, A. (2016). Exploiting publication contents and collaboration networks for collaborator recommendation. PLoS ONE, 11(2), e0148492.

    Article  Google Scholar 

  • Lee, S., & Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science, 35(5), 673–702.

    Article  Google Scholar 

  • Lee, D. H., Brusilovsky, P., & Schleyer, T. (2011). Recommending collaborators using social features and mesh terms. Proceedings of the Association for Information Science and Technology, 48(1), 1–10.

    Google Scholar 

  • Ley, M. (2002). The DBLP computer science bibliography: Evolution, research issues, perspectives. In String processing and information retrieval (pp. 1–10). Berlin: Springer.

  • Li, J., Xia, F., Wang, W., Chen, Z., Asabere, N. Y., Jiang, H. (2014). Acrec: A co-authorship based random walk model for academic collaboration recommendation. In Proceedings of the companion publication of the 23rd international conference on World wide web companion (pp. 1209–1214). International World Wide Web Conferences Steering Committee.

  • Liang, H., Xu, Y., Tjondronegoro, D., & Christen, P. (2012). Time-aware topic recommendation based on micro-blogs. In Proceedings of the 21st ACM international conference on information and knowledge management (pp. 1657–1661). ACM.

  • Lopes, G. R., Moro, M. M., Wives, L. K., & De Oliveira, J. P. M. (2010). Collaboration recommendation on academic social networks. In J. Trujillo, et al. (Eds.), Advances in conceptual modeling—Applications and challenges. ER 2010. Lecture Notes in Computer Science (Vol. 6413, pp. 190–199). Berlin: Springer.

    Google Scholar 

  • Lü, L., & Zhou, T. (2011). Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications, 390(6), 1150–1170.

    Article  Google Scholar 

  • Mosa, A. S. M., & Yoo, I. (2014). Association mining of search tags in PubMed search sessions. In 2014 IEEE international conference on bioinformatics and biomedicine (BIBM) (pp. 56–61). IEEE.

  • Opsahl, T. (2013). Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. Social Networks, 35(2), 159–167.

    Article  Google Scholar 

  • Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In The adaptive web (Vol. 4321, pp. 325–341). Berlin: Springer.

  • Pham, M. C., Cao, Y., Klamma, R., & Jarke, M. (2011). A clustering approach for collaborative filtering recommendation using social network analysis. Journal of Universal Computer Science, 17(4), 583–604.

    Google Scholar 

  • Saad, G. (2006). Exploring the h-index at the author and journal levels using bibliometric data of productive consumer scholars and business-related journals respectively. Scientometrics, 69(1), 117–120.

    Article  Google Scholar 

  • Smalheiser, N. R., & Torvik, V. I. (2009). Author name disambiguation. Annual Review of Information Science and Technology, 43(1), 1–43.

    Article  Google Scholar 

  • Sugiyama, K., & Kan, M. Y. (2010). Scholarly paper recommendation via user’s recent research interests. In Proceedings of the 10th annual joint conference on digital libraries (pp. 29–38). ACM.

  • Sun, J., Ma, J., Liu, X., Liu, Z., Wang, G., Jiang, H., et al. (2013). A novel approach for personalized article recommendation in online scientific communities. In 2013 46th Hawaii international conference on system sciences (HICSS) (pp. 1543–1552). IEEE.

  • Tang, J., Wu, S., Sun, J., & Su, H. (2012). Cross-domain collaboration recommendation. In Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1285–1293). ACM.

  • 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). ACM.

  • Tong, H., Faloutsos, C., & Pan, J.-Y. (2006). Fast random walk with restart and its applications. In Sixth international conference on data mining (ICDM’06) (pp. 613–622). IEEE.

  • Wang, X., & Sukthankar, G. (2013). Link prediction in multi-relational collaboration networks. In Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining (pp. 1445–1447). ACM.

  • Wang, W., Cui, Z., Gao, T., Yu, S., Kong, X., & Xia, F. (2017). Is scientific collaboration sustainability predictable? In Proceedings of the 26th international conference on world wide web companion (pp. 853–854). International World Wide Web Conferences Steering Committee.

  • Wang, W., Liu, J., Xia, F., King, I., & Tong, H. (2017). Shifu: Deep learning based advisor–advisee relationship mining in scholarly big data. In Proceedings of the 26th international conference on world wide web companion (pp. 303–310). International World Wide Web Conferences Steering Committee.

  • Wu, Z., Wu, J., Khabsa, M., Williams, K., Chen, H.-H., Huang, W., et al. (2014). Towards building a scholarly big data platform: Challenges, lessons and opportunities. In Digital Libraries (JCDL), 2014 IEEE/ACM joint conference on digital library (pp. 117–126). IEEE.

  • Xia, F., Chen, Z., Wang, W., Li, J., & Yang, L. T. (2014). Mvcwalker: Random walk-based most valuable collaborators recommendation exploiting academic factors. IEEE Transactions on Emerging Topics in Computing, 2(3), 364–375.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Yang, Z., Yin, D., & Davison, B. D. (2014). Recommendation in academia: A joint multi-relational model. In 2014 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM) (pp. 566–571). IEEE.

  • Yu, J., Zhao, H., Liu, F., & Xie, G. (2012). A method of discovering collaborative users based on psychological model in academic recommendation. In 2012 IEEE 12th international conference on computer and information technology (CIT) (pp. 1076–1081). IEEE.

  • Zhao, T., Zhao, H. V., & King, I. (2015). Exploiting game theoretic analysis for link recommendation in social networks. In Proceedings of the 24th ACM international on conference on information and knowledge management (pp. 851–860). ACM.

Download references

Acknowledgements

The study is partially supported by the Graduate Education Reform Fund of DUT (JG2016022).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhenzhen Xu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kong, X., Jiang, H., Wang, W. et al. Exploring dynamic research interest and academic influence for scientific collaborator recommendation. Scientometrics 113, 369–385 (2017). https://doi.org/10.1007/s11192-017-2485-9

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-017-2485-9

Keywords

Navigation