Skip to main content
SpringerLink
Log in

Link prediction in research collaboration: a multi-network representation learning framework with joint training

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

With the rapid advancement of scientific research, collaboration in this area is becoming increasingly important. One of the major challenges is the link prediction problem for research collaboration. Recently, learning-based link prediction methods have received much attention. However, most of these studies have solely concentrated on exploiting a single network and its topology features for prediction, and ignore other factors that may influence link formation. To address this issue, in this paper we propose a link prediction model based on multi-network representation learning. Specifically, we develop new features based on the author’s institutions and published papers, and three networks incorporating these features are modeled. Then, the network representation method based on joint training is proposed to embed the networks in a low-dimensional space. Finally, the authors’ feature vectors are combined in finer granularity, and collaboration prediction is performed in a supervised manner. The performance of our model is evaluated by comparing it with other link prediction methods on a real-world dataset, and the experimental results show the effectiveness of our model.

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

Fig1
Fig2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. https://github.com/thunlp/OpenNE.

References

  1. Adamic LA, Adar E (2003) Friends and neighbors on the Web. Social Networks 25:211–230. https://doi.org/10.1016/S0378-8733(03)00009-1

    Article  Google Scholar 

  2. Ahmed A, Shervashidze N, Narayanamurthy S, et al (2013) Distributed large-scale natural graph factorization. In: Proceedings of the 22nd international conference on World Wide Web. Association for Computing Machinery, New York, NY, USA, pp 37–48

  3. Ahmed C, ElKorany A, Bahgat R (2016) A supervised learning approach to link prediction in Twitter. Soc Netw Anal Min 6:24. https://doi.org/10.1007/s13278-016-0333-1

    Article  Google Scholar 

  4. Aldieri L, Kotsemir M, Vinci CP (2018) The impact of research collaboration on academic performance: An empirical analysis for some European countries. Socioecon Plann Sci 62:13–30. https://doi.org/10.1016/j.seps.2017.05.003

    Article  Google Scholar 

  5. Aziz F, Gul H, Uddin I, Gkoutos GV (2020) Path-based extensions of local link prediction methods for complex networks. Sci Rep 10:19848. https://doi.org/10.1038/s41598-020-76860-2

    Article  Google Scholar 

  6. Barabási AL, Jeong H, Néda Z et al (2002) Evolution of the social network of scientific collaborations. Physica A 311:590–614. https://doi.org/10.1016/S0378-4371(02)00736-7

    Article  MathSciNet  MATH  Google Scholar 

  7. Belkin M, Niyogi P (2003) Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Comput 15:1373–1396. https://doi.org/10.1162/089976603321780317

    Article  MATH  Google Scholar 

  8. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech 2008:P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008

    Article  MATH  Google Scholar 

  9. Brin S, Page L (1998) The anatomy of a large-scale hypertextual Web search engine. Comput Netw ISDN Syst 30:107–117. https://doi.org/10.1016/S0169-7552(98)00110-X

    Article  Google Scholar 

  10. Cao J, Lin X, Guo S, et al (2021) Bipartite Graph Embedding via Mutual Information Maximization. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, New York, NY, USA, pp 635–643

  11. Cao S, Lu W, Xu Q (2015) GraRep: Learning Graph Representations with Global Structural Information. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Association for Computing Machinery, New York, NY, USA, pp 891–900

  12. Chuan PM, Son LH, Ali M et al (2018) Link prediction in co-authorship networks based on hybrid content similarity metric. Appl Intell 48:2470–2486. https://doi.org/10.1007/s10489-017-1086-x

    Article  Google Scholar 

  13. Cohen S, Ebel L (2013) Recommending collaborators using keywords. In: Proceedings of the 22nd International Conference on World Wide Web. Association for Computing Machinery, New York, NY, USA, pp 959–962

  14. 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. Association for Computing Machinery, New York, NY, USA, pp 855–864

  15. Hasan MA, Chaoji V, Salem S, Zaki M (2006) Link prediction using supervised learning. In: Proceedings of SDM Workshop on Link Analysis, Counter-terrorism and Security. SDM, Minneapolis, MN, USA, pp 798–805

  16. Hassan D (2019) Supervised link prediction in co-authorship networks based on research performance and similarity of research interests and affiliations. In: 2019 International Conference on Machine Learning and Cybernetics (ICMLC). ICMLC, Kobe, Japan, pp 1–6. https://doi.org/10.1109/ICMLC48188.2019.8949320

  17. Jin T, Wu Q, Ou X, Yu J (2021) Community detection and co-author recommendation in co-author networks. Int J Mach Learn & Cyber 12:597–609. https://doi.org/10.1007/s13042-020-01190-8

    Article  Google Scholar 

  18. Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18:39–43. https://doi.org/10.1007/BF02289026

    Article  MATH  Google Scholar 

  19. Katz JS (1994) Geographical proximity and scientific collaboration. Scientometrics 31:31–43. https://doi.org/10.1007/BF02018100

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Kumar A, Singh SS, Singh K, Biswas B (2020) Link prediction techniques, applications, and performance: A survey. Phys A: Stat Mech Appl 553:124289. https://doi.org/10.1016/j.physa.2020.124289

    Article  MathSciNet  MATH  Google Scholar 

  22. Li J, Xia F, Wang W, et al (2014) ACRec: a co-authorship based random walk model for academic collaboration recommendation. In: Proceedings of the 23rd International Conference on World Wide Web. Association for Computing Machinery, New York, NY, USA, pp 1209–1214

  23. Li K, Tu L, Chai L (2020) Ensemble-model-based link prediction of complex networks. Comput Netw 166:106978. https://doi.org/10.1016/j.comnet.2019.106978

    Article  Google Scholar 

  24. Liang W, Zhou X, Huang S et al (2018) Modeling of cross-disciplinary collaboration for potential field discovery and recommendation based on scholarly big data. Futur Gener Comput Syst 87:591–600. https://doi.org/10.1016/j.future.2017.12.038

    Article  Google Scholar 

  25. Lin S, Hong W, Wang D, Li T (2017) A survey on expert finding techniques. J Intell Inf Syst 49:255–279. https://doi.org/10.1007/s10844-016-0440-5

    Article  Google Scholar 

  26. Maisonobe M, Eckert D, Grossetti M et al (2016) The world network of scientific collaborations between cities: domestic or international dynamics? J Informet 10:1025–1036. https://doi.org/10.1016/j.joi.2016.06.002

    Article  Google Scholar 

  27. Makarov I, Gerasimova O (2019) Predicting collaborations in co-authorship network. In: 14th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP). SMAP, Larnaca, Cyprus, pp 1–6. https://doi.org/10.1109/SMAP.2019.8864887

  28. Makarov I, Gerasimova O, Sulimov P, Zhukov LE (2019) Dual network embedding for representing research interests in the link prediction problem on co-authorship networks. PeerJ Comput Sci 5:e172. https://doi.org/10.7717/peerj-cs.172

    Article  Google Scholar 

  29. Malhotra D, Goyal R (2021) Supervised-learning link prediction in single layer and multiplex networks. Mach Learn Appl 6:100086. https://doi.org/10.1016/j.mlwa.2021.100086

    Article  Google Scholar 

  30. Mayrose I, Freilich S (2015) The Interplay between Scientific Overlap and Cooperation and the Resulting Gain in Co-Authorship Interactions. PLoS ONE 10:e0137856. https://doi.org/10.1371/journal.pone.0137856

    Article  Google Scholar 

  31. Mikolov T, Chen K, Corrado G, Dean J. (2013). Efficient estimation of word representations in vector space. In: International Conference on Learning Representations (ICLR). ICLR, Scottsdale, Arizona, USA, pp 4–5. https://doi.org/10.48550/arXiv.1301.3781

  32. Newman MEJ (2001) Clustering and preferential attachment in growing networks. Phys Rev E 64:025102. https://doi.org/10.1103/PhysRevE.64.025102

    Article  Google Scholar 

  33. Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. Association for Computing Machinery, New York, NY, USA, pp 701–710

  34. Pradhan T, Pal S (2020) A multi-level fusion based decision support system for academic collaborator recommendation. Knowl-Based Syst 197:105784. https://doi.org/10.1016/j.knosys.2020.105784

    Article  Google Scholar 

  35. Rahman M, Saha TK, Hasan MA et al (2018) Dylink2vec: effective feature representation for link prediction in dynamic networks. arXiv. https://doi.org/10.48550/arXiv.1804.05755

  36. Robertson S (2004) Understanding inverse document frequency: on theoretical arguments for IDF. J Doc 60:503–520. https://doi.org/10.1108/00220410410560582

    Article  Google Scholar 

  37. Sun K, Wang L, Xu B et al (2020) Network Representation Learning: From Traditional Feature Learning to Deep Learning. IEEE Access 8:205600–205617. https://doi.org/10.1109/ACCESS.2020.3037118

    Article  Google Scholar 

  38. Tang J, Qu M, Wang M, et al (2015) LINE: Large-scale Information Network Embedding. In: Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, pp 1067–1077

  39. Tong H, Faloutsos C, Pan J (2006) Fast Random Walk with Restart and Its Applications. In: Sixth International Conference on Data Mining (ICDM’06). pp 613–622

  40. Wang D, Cui P, Zhu W (2016) Structural Deep Network Embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, New York, NY, USA, pp 1225–1234

  41. Wang S, Tang J, Aggarwal C, Liu H (2016) Linked Document Embedding for Classification. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. Association for Computing Machinery, New York, NY, USA, pp 115–124

  42. Wang W, Xia F, Wu J, et al (2021) Scholar2vec: Vector Representation of Scholars for Lifetime Collaborator Prediction. ACM Trans Knowl Discov Data 15:40:1-40:19. https://doi.org/10.1145/3442199

  43. Wang X, Chai Y, Li H, Wu D (2021) Link prediction in heterogeneous information networks: An improved deep graph convolution approach. Decis Support Syst 141:113448. https://doi.org/10.1016/j.dss.2020.113448

    Article  Google Scholar 

  44. Wang X, He X, Wang M, et al (2019) Neural Graph Collaborative Filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, pp 165–174

  45. West JD, Wesley-Smith I, Bergstrom CT (2016) A Recommendation System Based on Hierarchical Clustering of an Article-Level Citation Network. IEEE Trans Big Data 2:113–123. https://doi.org/10.1109/TBDATA.2016.2541167

    Article  Google Scholar 

  46. Xie M, Yin H, Wang H, et al (2016) Learning Graph-based POI Embedding for Location-based Recommendation. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. Association for Computing Machinery, New York, NY, USA, pp 15–24

  47. Xu K, Li C, Tian Y, et al (2018) Representation Learning on Graphs with Jumping Knowledge Networks. In: Proceedings of the 35th International Conference on Machine Learning. PMLR, pp 5453–5462

  48. Xu Z, Yuan Y, Wei H, Wan L (2019) A serendipity-biased Deepwalk for collaborators recommendation. PeerJ Comput Sci 5:e178. https://doi.org/10.7717/peerj-cs.178

    Article  Google Scholar 

  49. Yan M, Jing N, Zhong Z, Wu Y (2019) Geographical Entity Community Mining Based on Spatial and Semantic Association. In: Proceedings of the 3rd International Conference on Computer Science and Application Engineering. Association for Computing Machinery, New York, NY, USA, pp 1–6

  50. Yang C, Liu T, Chen X et al (2020) HNRWalker: recommending academic collaborators with dynamic transition probabilities in heterogeneous networks. Scientometrics 123:429–449. https://doi.org/10.1007/s11192-020-03374-z

    Article  Google Scholar 

  51. Zhai C (2008) Statistical Language Models for Information Retrieval. Synthesis Lectures on Human Language Technologies 1:1–141. https://doi.org/10.2200/S00158ED1V01Y200811HLT001

  52. Zhang M, Chen Y (2018) Link prediction based on graph neural networks. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems (NeurIPS). Curran Associates Inc., Red Hook, NY, USA, pp 5171–5181. https://dl.acm.org/doi/10.5555/3327345.3327423

  53. Zhang Z, Hong W-C (2021) Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads. Knowl-Based Syst 228:107297. https://doi.org/10.1016/j.knosys.2021.107297

    Article  Google Scholar 

  54. Zhang Z, Cai J, Zhang Y, Wang J (2020) Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction. Proc AAAI Conf Artif Intell 34:3065–3072. https://doi.org/10.1609/aaai.v34i03.5701

    Article  Google Scholar 

  55. Zhang D, Yin J, Zhu X, Zhang C (2020) Network Representation Learning: A Survey. IEEE Trans Big Data 6:3–28. https://doi.org/10.1109/TBDATA.2018.2850013

    Article  Google Scholar 

  56. Zhou T, Lü L, Zhang Y-C (2009) Predicting missing links via local information. Eur Phys J B 71:623–630. https://doi.org/10.1140/epjb/e2009-00335-8

    Article  MATH  Google Scholar 

Download references

Funding

This work was supported by grants from National Natural Science Foundation of China [71901150, 71701134]; Guangdong Basic and Applied Basic Research Foundation [2023A1515012515, 2022A1515012077]; Guangdong Province Innovation Team "Intelligent Management and Interdisciplinary Innovation" [2021WCXTD002]; Shenzhen Higher Education Support Plan [20200826144104001].

Author information

Authors and Affiliations

  1. College of Management, Shenzhen University, Shenzhen, China

    Chen Yang, Chuhan Wang, Ruozhen Zheng & Shuang Geng

Authors
  1. Chen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chuhan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruozhen Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuang Geng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuang Geng.

Ethics declarations

Competing interests

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) 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

Yang, C., Wang, C., Zheng, R. et al. Link prediction in research collaboration: a multi-network representation learning framework with joint training. Multimed Tools Appl 82, 47215–47233 (2023). https://doi.org/10.1007/s11042-023-15720-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-15720-3

Keywords

Search

Navigation