, Volume 113, Issue 1, pp 369–385 | Cite as

Exploring dynamic research interest and academic influence for scientific collaborator recommendation

  • Xiangjie Kong
  • Huizhen Jiang
  • Wei Wang
  • Teshome Megersa Bekele
  • Zhenzhen XuEmail author
  • Meng Wang


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.


Collaborator recommendation Topic clustering Research interest variation Academic influence Feature matrix 



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


  1. Abramo, G., D’Angelo, C. A., & Di Costa, F. (2009). Research collaboration and productivity: Is there correlation? Higher Education, 57(2), 155–171.CrossRefGoogle Scholar
  2. 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.Google Scholar
  3. 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.Google Scholar
  4. 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.Google Scholar
  5. 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.Google Scholar
  6. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993–1022.zbMATHGoogle Scholar
  7. Brandão, M. A., & Moro, M. M. (2016). Social professional networks: A survey and taxonomy. Computer Communications. doi: 10.1016/j.comcom.2016.12.011.
  8. 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.CrossRefGoogle Scholar
  9. 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.Google Scholar
  10. Daud, A. (2012). Using time topic modeling for semantics-based dynamic research interest finding. Knowledge-Based Systems, 26, 154–163.CrossRefGoogle Scholar
  11. Davis, P., & Fromerth, M. (2007). Does the arxiv lead to higher citations and reduced publisher downloads for mathematics articles? Scientometrics, 71(2), 203–215.CrossRefGoogle Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.Google Scholar
  14. George, G., Haas, M. R., & Pentland, A. (2014). Big data and management. Academy of Management Journal, 57(2), 321–326.CrossRefGoogle Scholar
  15. 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.Google Scholar
  16. 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.Google Scholar
  17. 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.CrossRefzbMATHGoogle Scholar
  18. Kanhabua, N., & Nørvåg, K. (2010). Determining time of queries for re-ranking search results. In ECDL (Vol. 10, pp. 261–272).Google Scholar
  19. Katz, J. S., & Martin, B. R. (1997). What is research collaboration?. Research policy, 26(1), 1–18.CrossRefGoogle Scholar
  20. 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.CrossRefGoogle Scholar
  21. Lee, S., & Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science, 35(5), 673–702.CrossRefGoogle Scholar
  22. 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
  23. Ley, M. (2002). The DBLP computer science bibliography: Evolution, research issues, perspectives. In String processing and information retrieval (pp. 1–10). Berlin: Springer.Google Scholar
  24. 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.Google Scholar
  25. 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.Google Scholar
  26. 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
  27. Lü, L., & Zhou, T. (2011). Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications, 390(6), 1150–1170.CrossRefGoogle Scholar
  28. 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.Google Scholar
  29. Opsahl, T. (2013). Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. Social Networks, 35(2), 159–167.CrossRefGoogle Scholar
  30. Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In The adaptive web (Vol. 4321, pp. 325–341). Berlin: Springer.Google Scholar
  31. 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
  32. 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.CrossRefGoogle Scholar
  33. Smalheiser, N. R., & Torvik, V. I. (2009). Author name disambiguation. Annual Review of Information Science and Technology, 43(1), 1–43.CrossRefGoogle Scholar
  34. 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.Google Scholar
  35. 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.Google Scholar
  36. 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.Google Scholar
  37. 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.Google Scholar
  38. 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.Google Scholar
  39. 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.Google Scholar
  40. 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.Google Scholar
  41. 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.Google Scholar
  42. 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.Google Scholar
  43. 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.CrossRefGoogle Scholar
  44. Xia, F., Wang, W., Bekele, T. M., & Liu, H. (2017). Big scholarly data: A survey. IEEE Transactions on Big Data, 3(1), 18–35.CrossRefGoogle Scholar
  45. 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.Google Scholar
  46. 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.Google Scholar
  47. 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.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2017

Authors and Affiliations

  1. 1.Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of SoftwareDalian University of TechnologyDalianChina

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