Skip to main content
SpringerLink
Log in

Academic collaborations: a recommender framework spanning research interests and network topology

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Fruitful academic collaborations have become increasingly more important for solving scientific problems, participating in research projects, and improving productivity. As such, frameworks for recommending suitable collaborators are attracting extensive attention from scholars. In an effort to improve on the current solutions, we have developed an approach that produces recommendations with better precision, recall, and accuracy. Our strategy is to comprehensively consider the similarity of both scholars' research interests and their collaboration network topologies, leveraging the benefits of these two common similarity indicators into one unified collaborator recommendation framework. A Word2Vec model creates word embeddings of research interests, which solves the problem of calculating similarity solely based on co-occurrence, not context, while a Node2Vec model automatically extracts and learns the topological features of a co-authorship network, moving beyond just local features to capture global network features as well. Then the CombMNZ method is used to fuse the results of the two similarity measures. A ranked collaborator list is then generated to recommend potential collaborators to the target scholars. The workings of the framework and its benefits are demonstrated through a case study on academics in the field of intelligent driving and a comparison with the three baselines: Random Walk with Restart (RWR), Latent Dirichlet Allocation (LDA), and Researcher’s Interest Variation with Time (RIVT). Our framework should be of benefit to academics, research centers, and private-enterprise R&D managers who are seeking partners. We hope that, through the framework’s recommendations, collaborators will form strong partnerships and be able to achieve the ultimate goal of completing research projects, solving scientific problems, and promoting discipline development and progress.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Notes

  1. https://www.thevantagepoint.com/.

References

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

    Article  Google Scholar 

  • Abramo, G., D’Angelo, C. A., & Costa, F. (2012). Identifying interdisciplinary through the disciplinary classification of coauthors of scientific publications. Journal of the American Society for Information Science and Technology, 63(11), 2206–2222.

    Article  Google Scholar 

  • Ahsan, N., Williams, S. B., Jakuba, M., Pizarro, O., & Radford, B. (2010). Predictive habitat models from AUV-based multibeam and optical imagery. In OCEANS 2010 MTS/IEEE SEATTLE (pp.1–10).

  • Balabanovic, M., & Shoham, Y. (1997). Fab: Content-based, collaborative recommendation. Communication of the ACM, 40(3), 66–72.

    Article  Google Scholar 

  • Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. In Proceedings of International AAAI Conference on Web and Social Media (pp. 361–362).

  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2008). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(4–5), 993–1022.

    MATH  Google Scholar 

  • Cai, D., He, X., & Han, J. (2008). Training linear discriminant analysis in linear time. In 2008 IEEE 24th International Conference on Data Engineering (pp. 209–217).

  • Chen, J. Y., Wu, Y. Y., Fan, L., Lin, X., Zheng, H. B., Yu, S. Q., & Xuan, Q. (2017). Improved spectral clustering collaborative filtering with node2vec technology. In 2017 IEEE 14th International Workshop on Complex Systems and Networks (IWCSN) (pp. 330–334).

  • Cui, P., Wang, X., Pei, J., & Zhu, W. W. (2019). A survey on network embedding. IEEE Transactions on Knowledge and Data Engineering., 31(5), 833–852.

    Article  Google Scholar 

  • Deepika, S. S., & Geetha, T. V. (2018). A meta-learning framework using representation learning to predict drug-drug interaction. Journal of Biomedical Informatics, 84, 136–147.

    Article  Google Scholar 

  • Dehak, N., Kenny, P. J., Dehak, R., Dumouchel, P., & Ouellet, P. (2011). Front-end factor analysis for speaker verification. IEEE Transactions on Audio Speech and Language Processing., 19(4), 788–798.

    Article  Google Scholar 

  • Saari, D. G. (1999). Explaining all three-alternative voting outcomes. Journal of Economic Theory, 87(2), 313–355.

    Article  MathSciNet  MATH  Google Scholar 

  • Dong, Y., Tang, J., Wu, S., Tian, J. L., Chawla, N. V., Rao, J.H., & Cao, H. H. (2013). Link prediction and recommendation across heterogeneous social networks. In 2012 IEEE 12th International Conference on Data Mining (pp. 181–190).

  • Edward, A. F., & Joseph, A. S. (1994). Combination of multiple searches. NIST SPECIAL PUBLICATION, 243–243.

  • Eunice, T., Iris, S., Humphrey, L., & Yiu-Kai, N. (2016). Making personalized movie recommendations for children. In Proceedings of 18th International Conference on Information Integration & Web-based Applications & Services (pp. 96–105).

  • Faleiros, T. D. P., & Lopes, A. D. A. (2015). Bipartite graph for topic extraction. In Twenty-Fourth International Joint Conference on Artificial Intelligence (pp. 4361–4362).

  • Gang, L., Li, L., Jin, M., & Ye, G. (2015). Empirical research on similarity of research interests in co-authorship network. Library and Information Service, 59(2), 75.

    Google Scholar 

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

    Article  Google Scholar 

  • Glänzel, W., & Czerwon, H. (1996). A new methodological approach to bibliographic coupling and its application to the national, regional and institutional level. Scientometrics, 37(2), 195–221.

    Article  Google Scholar 

  • Gollapalli, S., Mitra, P., & Giles, C. (2012). Similar researcher search in academic environments. In Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (pp. 167–170). https://doi.org/10.1145/2232817.2232849.

  • Grover, A., & Leskovec, J. (2016). node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 855–864).

  • Guns, R., & Rousseau, R. (2014). Recommending research collaborations using link prediction and random forest classifiers. Scientometrics, 101, 1461–1473.

    Article  Google Scholar 

  • Hildrun, K. (2004). Author productivity and geodesic distance in bibliographic co-authorship networks, and visibility on the Web. Scientometrics, 60(3), 409–420.

    Article  Google Scholar 

  • Hollinger, G. A., Choudhary, S., Qarabaqi, P., Murphy, C., Mitra, U., Sukhatme, G. S., Stojanovic, M., Singh, H., & Hover, F. (2011). Communication protocols for underwater data collection using a robotic sensor network. In Proceedings of the IEEE GLOBECOM Workshops (pp.1308–1313).

  • Hu, F., Liu, J., Li, L. H., & Liang, J. (2019). Community detection in complex networks using Node2vec with spectral clustering. Physica A: Statistical Mechanics and Its Applications, 545(1), 123633.

    Google Scholar 

  • Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18. https://doi.org/10.1016/S0048-7333(96)00917-1

    Article  Google Scholar 

  • Kawamae, N. (2010). Latent interest-topic model: finding the causal relationships behind dyadic data. In Proceedings of the 19th ACM international conference on Information and knowledge management (pp. 649–658).

  • Kazemi, B., & Abhari, A. (2020). Content-based Node2Vec for representation of papers in the scientific literature. Data & Knowledge Engineering, 127(5), 101794.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Krishnamurthy, B., Puri, N., & Goel, R. (2016). Learning Vector-space representations of items for recommendations using word embedding models. Procedia Computer Science, 80, 2205–2210.

    Article  Google Scholar 

  • Kwon, S., Liu, X., Porter, A. L., & Youtie, J. (2019). Research addressing emerging technological ideas has greater scientific impact. Research Policy, 48(9), 103834.

    Article  Google Scholar 

  • Lab, D. N., & Tollison, R. D. (2000). Intellectual collaboration. Journal of Political Economy, 108(3), 632–661.

    Article  Google Scholar 

  • Le, Q., & Mikolov, T. (2014). Distributed representations of sentences and documents. In Proceedings of the 31st International Conference on Machine Learning (ICML-14) (pp. 1188–1196).

  • Lee, S., & Bozeman, B. (2003). The impact of research collaboration on scientific productivity. Social Studies of Science, 35(5), 673–702.

    Article  Google Scholar 

  • Li, C., Guo, J., Lu, Y., Wu, J., & Liu, P. (2018a). LDA meets Word2Vec: A novel model for academic abstract clustering. Companion of the The Web Conference (pp. 1699–1706).

  • Liben-Nowell, D., & Kleinberg, J. (2007). The link prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58(7), 1019–1031.

    Article  Google Scholar 

  • Lilleberg, J., Zhu, Y., & Zhang, Y. (2015). Support vector machines and Word2vec for text classification with semantic features. In IEEE International Conference on Cognitive Informatics & Cognitive Computing (pp. 136–140).

  • Lopes, G. R., Moro, M. M., Wives, L. K., & de Oliveira, J. P. M. (2010). Collaboration recommendation on academic social networks. In Proceedings of the 29th International Conference on Conceptual Modeling (pp. 190–+).

  • Li, C. Z., Lu, Y., Wu, J. F., Zhang, Y. R., Xia, Z. Z., Wang, T. C., Yu, D. T., Chen, X. R., Liu, P. D., & Guo, J. Y. (2018b). LDA Meets Word2Vec: A novel model for academic abstract clustering. In Proceedings of the 27th World Wide Web (WWW) Conference (pp. 1699–1706).

  • Li, L., Wang, W., Yu, S., Wan, L., Xu, Z., & Kong, X. (2017). A modified Node2vec method for disappearing link prediction. In 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (pp. 1232–1235).

  • Liu, Y. Z., Tian, Z. Q., Sun, J. S., Jiang, Y. C., & Zhang, X. (2020). Distributed representation learning via node2vec for implicit feedback recommendation. Neural Computing & Applications, 32(9), 4335–4345.

    Article  Google Scholar 

  • Lv, L., & Zhou, T. (2010). Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and Its Applications, 390, 1150–1170.

    Google Scholar 

  • Macdonald, C., & Ounis, I. (2008). Voting techniques for expert search. Knowledge & Information Systems, 16(3), 259–280.

    Article  Google Scholar 

  • Man, T., Shen, H., Liu, S., Jin, X., & Cheng, X. (2016). Predict anchor links across social networks via an embedding approach. In International Joint Conference on Artificial Intelligence (pp. 1823–1829).

  • Matveev, A. S., Wang, C., & Savkin, A. V. (2012). Real-time navigation of mobile robots in problems of border patrolling and avoiding collisions with moving and deforming obstacles. Robotics & Autonomous Systems, 60(6), 769–788.

    Article  Google Scholar 

  • Melin, G. (2000). Pragmatism and self-organization: Research collaboration on the individual level. Research Policy, 29(1), 31–40.

    Article  Google Scholar 

  • Merton, R. K. (1973). The sociology of science: Theoretical and empirical investigations. University of Chicago Press.

    Google Scholar 

  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013a). Efficient estimation of word representations in vector space. Computer Science, 2(12), 27–35.

    Google Scholar 

  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013b). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems (pp. 3111–3119).

  • Mimno, D., & McCallum, A. (2007). Expertise modeling for matching papers with reviewers. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 500–509).

  • 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 

  • Ping, N., & De-Gen, H. (2016). TF-IDF and rules based automatic extraction of Chinese keywords. Journal of Chinese Computer Systems, 37(4), 711–715.

    Google Scholar 

  • Pradhan, T., Sahoo, S., Singh, U., & Pal, S. (2020). A proactive decision support system for reviewer recommendation in academia. Expert Systems with Applications, 169, 114331.

    Article  Google Scholar 

  • Pradhan, T., & Pal, S. (2020). A multi-level fusion based decision support system for academic collaborator recommendation. Knowledge-Based Systems, 197, 1–23.

    Article  Google Scholar 

  • Price, D. (1963). Little science, big science. Columbia University Press.

    Book  Google Scholar 

  • Rajaraman, A., & Ullman, J. D. (2011). Mining of massive datasets. Cambridge University Press.

    Book  Google Scholar 

  • Rosen-Zvi, M., Chemudugunta, C., Griffiths, T., Smyth, P., & Steyvers, M. (2010). Learning author-topic models from text corpora. ACM Transactions on Information Systems (TOIS), 28(1), 1–38.

    Article  Google Scholar 

  • Shibata, N., Kajikawa, Y., & Sakata, I. (2012). Link prediction in citation networks. Journal of the American Society for Information Science and Technology, 63(1), 78–85.

    Article  Google Scholar 

  • Smith, R. N., Cazzaro, D., Invernizzi, L., Marani, G., Choi, S. K., & Chyba, M. (2011). A geometric approach to trajectory design for an autonomous underwater vehicle: Surveying the bulbous bow of a ship. Acta Applicandae Mathematicae, 115(2), 209–232.

    Article  MathSciNet  MATH  Google Scholar 

  • Sooho, L., & Barry, B. (2005). Scientific collaboration||the impact of research collaboration on scientific productivity. Social Studies of Science, 35(5), 673–702.

  • Tang, L. (2013). Does “birds of a feather flock together”matter-Evidence from a longitudinal study on US–China scientific collaboration. Journal of Informetrics, 7(2), 330–344.

    Article  Google Scholar 

  • Taşcı, Ş, & Güngör, T. (2013). Comparison of text feature selection policies and using an adaptive framework. Expert Systems with Applications, 40(12), 4871–4886.

    Article  Google Scholar 

  • Wang, X. F., Zhang, S., & Liu, Y. Q. (2021). ITGInsight-discovering and visualizing research fronts in the scientific literature. Scientometrics. https://doi.org/10.1007/s11192-021-04190-9

    Article  Google Scholar 

  • Wang, Z., Long, M., & Zhang, Y. (2016). A hybrid document feature extraction method using latent Dirichlet allocation and Word2Vec. In 2016 IEEE First International Conference on Data Science in Cyberspace (DSC) (pp. 98–103). IEEE.

  • Weng, J., Lim, E. P., Jiang, J., & He, Q. (2010). Twitterrank:finding topic-sensitive influential twitterers. In Proceedings of the Third ACM International Conference on Web Search and Data Mining (pp. 261–270).

  • Widyotriatmo, A., & Hong, K. S. (2011). Navigation function-based control of multiple wheeled vehicles. IEEE Transactions on Industrial Electronics, 58(5), 1896–1906.

    Article  Google Scholar 

  • Williams, S. B., Pizarro, O., Webster, J. M., Beaman, R. J., Mahon, I., Johnson-Roberson, M., & Bridge, T. C. L. (2010). Autonomous underwater vehicle–assisted surveying of drowned reefs on the shelf edge of the great barrier reef, australia. Journal of Field Robotics, 27(5), 675–697.

    Article  Google Scholar 

  • Xi, X. W., Guo, Y., & Duan, W. Y. (2021). Recommendation of academic collaborators: a methodology incorporating word embedding and network embedding. In Proceedings of the 1st Workshop on AI + Informetrics (AII2021) co-located with the iConference 2021 (pp. 47–57).

  • 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 

  • Xu, S., Shi, Q., Qiao, X., Zhu, L., Jung, H., Lee, S., & Choi, S. P. (2014). Author-Topic over Time (AToT): a dynamic users’ interest model. In Mobile, ubiquitous, and intelligent computing (pp. 239–245). Springer.

  • Yan, E., & Guns, R. (2014). Predicting and recommending collaborations: An author-, institution-, and country-level analysis. Journal of Informetrics, 8(2), 295–309.

    Article  Google Scholar 

  • Zhang, Q., Xu, X., Zhu, Y., & Zhou, T. (2015). Measuring multiple evolution mechanisms of complex networks. Scientific Reports, 5, 10350.

    Article  Google Scholar 

  • Zhang, J. (2017). Research collaboration prediction and recommendation based on network embedding in co-authorship networks. Proceedings of the Association for Information Science & Technology, 54(1), 847–849.

    Article  Google Scholar 

  • Zhang, Y., Lu, J., Liu, F., Liu, Q., Porter, A., Chen, H. S., & Zhang, G. Q. (2018). Does deep learning help topic extraction? A kernel k-means clustering method with word embedding. Journal of Informetrics, 12(4), 1099–1117.

    Article  Google Scholar 

  • Zhang, Y., Porter, A. L., Hu, Z., Guo, Y., & Newman, N. C. (2014). “Term clumping” for technical intelligence: A case study on dye-sensitized solar cells. Technological Forecasting and Social Change., 85, 26–39.

    Article  Google Scholar 

  • Ziman, J. M. (1994). Prometheus bound. Cambridge University Press.

    Book  Google Scholar 

  • Zuckerman, H. A. (1968). Patterns of Name Ordering Among Authors of Scientific Papers: A Study of Social Symbolism and Its Ambiguity. American Journal of Sociology, 74(3), 276–291.

    Article  Google Scholar 

Download references

Acknowledgements

This paper was supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. 72274219, 71874013 and 71810107004) and Program for Qian Duansheng Excellent Researcher in China University of Political Science and Law. The previous version of this work is published on Artificial Intelligence + Informetrics (AII) 2021 Workshop (Xi et al., 2021). The authors are very grateful for the valuable comments and suggestions from reviewers, which significantly improved the quality of the paper.

Author information

Authors and Affiliations

  1. Archives of Chinese Academy of Sciences, Beijing, China

    Xiaowen Xi

  2. China University of Political Science and Law, Beijing, China

    Jiaqi Wei, Ying Guo & Weiyu Duan

Authors
  1. Xiaowen Xi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiaqi Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weiyu Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [XX], [JW] and [WD]. Formulation or evolution of overarching research goals and oversight and leadership responsibility for the research activity planning and execution was performed by [YG]. The first draft of the manuscript was written by [XX] and all authors commented on previous versions of the manuscript.

Corresponding author

Correspondence to Ying Guo.

Ethics declarations

Conflict of interest

The authors declare that they have not conflict of interest, and actual or potential financial interests also.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xi, X., Wei, J., Guo, Y. et al. Academic collaborations: a recommender framework spanning research interests and network topology. Scientometrics 127, 6787–6808 (2022). https://doi.org/10.1007/s11192-022-04555-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-022-04555-8

Keywords

Search

Navigation