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HNRWalker: recommending academic collaborators with dynamic transition probabilities in heterogeneous networks

  • Chen Yang
  • Tingting Liu
  • Xiaohong Chen
  • Yiyang BianEmail author
  • Yuewen Liu
Article
  • 11 Downloads

Abstract

Multi-source information not only helps to solve the problem of sparse data but also improves recommendation performance in terms of personalization and accuracy. However, how to utilize it for facilitating academic collaboration effectively has been little studied in previous studies. Traditional mechanisms such as random walk algorithms are often assumed to be static which ignores crucial features of the linkages among various nodes in multi-source information networks. Therefore, this paper builds a heterogeneous network constructed by institution network and co-author network and proposes a novel random walk model for academic collaborator recommendation. Specifically, four neighbor relationships and the corresponding similarity assessment measures are identified according to the characteristics of different relationships in the heterogeneous network. Further, an improved random walk algorithm known as “Heterogeneous Network-based Random Walk” (HNRWalker) with dynamic transition probability and a new rule for selecting candidates are proposed. According to our validation results, the proposed method performs better than the benchmarks in improving recommendation performances.

Keywords

Collaborator recommendation services Heterogeneous networks Random walk algorithms Link prediction Academic social platforms 

Notes

Acknowledgements

This research was supported by grants from National Natural Science Foundation of China [71701134], Humanity and Social Science Youth Foundation of Ministry of Education of China [16YJC630153], Guangdong Basic and Applied Basic Research Foundation [2019A1515011392] and Natural Science Foundation of Guangdong Province of China [2017A030310427].

References

  1. Adamic, L. A., & Adar, E. (2003). Friends and neighbors on the web. Social networks,25(3), 211–230.CrossRefGoogle Scholar
  2. Alshareef, A. M., Alhamid, M. F., & El Saddik, A. (2018). Recommending Scientific Collaboration Based on Topical, Authors and Venues Similarities. In Paper presented at the 2018 IEEE international conference on information reuse and integration (IRI) (pp. 55–61). IEEE.Google Scholar
  3. Bergé, L. R. (2017). Network proximity in the geography of research collaboration. Papers in Regional Science,96(4), 785–815.Google Scholar
  4. Bornmann, L., & Leydesdorff, L. (2015). Topical connections between the institutions within an organisation (institutional co-authorships, direct citation links and co-citations). Scientometrics,102(1), 455–463.CrossRefGoogle Scholar
  5. Brandao, M. A., & Moro, M. M. (2012). Affiliation influence on recommendation in academic social networks. In Paper presented at the AMW (pp. 230–234).Google Scholar
  6. Chaiwanarom, P., & Lursinsap, C. (2015). Collaborator recommendation in interdisciplinary computer science using degrees of collaborative forces, temporal evolution of research interest, and comparative seniority status. Knowledge-Based Systems,75, 161–172.CrossRefGoogle Scholar
  7. Chuan, P. M., Ali, M., Khang, T. D., & Dey, N. (2018). Link prediction in co-authorship networks based on hybrid content similarity metric. Applied Intelligence,48(8), 2470–2486.CrossRefGoogle Scholar
  8. Cohen, S., & Ebel, L. (2013). Recommending collaborators using keywords. In Paper presented at the proceedings of the 22nd international conference on World Wide Web (pp. 959–962). ACM.Google Scholar
  9. Davoodi, E., Kianmehr, K., & Afsharchi, M. (2013). A semantic social network-based expert recommender system. Applied Intelligence,39(1), 1–13.CrossRefGoogle Scholar
  10. Du, L., Li, C., Chen, H., Tan, L., & Zhang, Y. (2015). Probabilistic SimRank computation over uncertain graphs. Information Sciences,295, 521–535.MathSciNetzbMATHCrossRefGoogle Scholar
  11. Fang, W., Yang, G., & Hu, Z. (2018). An improved DV-Hop algorithm with Jaccard coefficient based on optimization of distance correction. In Paper presented at the international conference on bio-inspired computing: theories and applications (pp. 457–465). Springer.Google Scholar
  12. Guo, Y., & Chen, X. (2014). Cross-domain scientific collaborations prediction with citation information. In Paper presented at the 2014 IEEE 38th international computer software and applications conference workshops (pp. 229–233). IEEE.Google Scholar
  13. Hoang, D. T., Nguyen, N. T., Tran, V. C., & Hwang, D. (2019). Research collaboration model in academic social networks. Enterprise Information Systems,13(7–8), 1023–1045.CrossRefGoogle Scholar
  14. Hoekman, J., Frenken, K., & Van Oort, F. (2009). The geography of collaborative knowledge production in Europe. The Annals of Regional Science,43(3), 721–738.CrossRefGoogle Scholar
  15. Huang, J., & Ling, C. X. (2005). Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on knowledge Data Engineering,17(3), 299–310.CrossRefGoogle Scholar
  16. Huynh, T., Hoang, K., & Lam, D. (2013). Trend based vertex similarity for academic collaboration recommendation. In Paper presented at the international conference on computational collective intelligence (pp. 11–20). Springer.Google Scholar
  17. Jung, J., Shin, K., Sael, L., & Kang, U. (2016). Random walk with restart on large graphs using block elimination. ACM Transactions on Database Systems,41(2), 1–43.  https://doi.org/10.1145/2901736.MathSciNetCrossRefGoogle Scholar
  18. Khan, S., Liu, X., Shakil, K. A., & Alam, M. (2017). A survey on scholarly data: From big data perspective. Information Processing Management,53(4), 923–944.CrossRefGoogle Scholar
  19. Lee, J., Oh, S., Dong, H., Wang, F., & Burnett, G. (2019). Motivations for self-archiving on an academic social networking site: A study on researchgate. Journal of the Association for Information Science Technology,70(6), 563–574.CrossRefGoogle Scholar
  20. Li, Z., Liang, X., Zhou, X., Zhang, H., & Ma, Y. (2016). A link prediction method for large-scale networks. Chinese Journal of Computers,39(42), 1–18.MathSciNetGoogle Scholar
  21. Li, S., Song, X., Lu, H., Zeng, L., Shi, M., & Liu, F. (2020). Friend recommendation for cross marketing in online brand community based on intelligent attention allocation link prediction algorithm. Expert Systems with Applications,139, 112839.  https://doi.org/10.1016/j.eswa.2019.112839.CrossRefGoogle Scholar
  22. 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 Paper presented at the proceedings of the 23rd international conference on World Wide Web (pp. 1209–1214). ACM.Google Scholar
  23. Liu, Z., & Jansen, B. J. (2017). Identifying and predicting the desire to help in social question and answering. Information Processing Management,53(2), 490–504.CrossRefGoogle Scholar
  24. Luong, N. T., Nguyen, T. T., Jung, J. J., & Hwang, D. (2015). Discovering co-author relationship in bibliographic data using similarity measures and random walk model. In Paper presented at the Asian conference on intelligent information and database systems (pp. 127–136). Springer.Google Scholar
  25. Mahapatra, R., Samanta, S., Pal, M., & Xin, Q. (2019). RSM index: A new way of link prediction in social networks. Journal of Intelligent Fuzzy Systems (Preprint), pp. 1–15.Google Scholar
  26. Makarov, I., Bulanov, O., & Zhukov, L. E. (2016). Co-author recommender system. In Paper presented at the international conference on network analysis (pp. 251–257). Springer.Google Scholar
  27. Montefusco, A. M., do Nascimento, F. P., Sennes, L. U., Bento, R. F., & Imamura, R. (2019). Influence of international authorship on citations in Brazilian medical journals: a bibliometric analysis. Scientometrics,119(3), 1487–1496.CrossRefGoogle Scholar
  28. Ortega, J. L., & Aguillo, I. F. (2013). Institutional and country collaboration in an online service of scientific profiles: Google Scholar Citations. Journal of Informetrics,7(2), 394–403.CrossRefGoogle Scholar
  29. Ostroumova Prokhorenkova, L., & Samosvat, E. (2016). Recency-based preferential attachment models. Journal of Complex Networks,4(4), 475–499.MathSciNetGoogle Scholar
  30. Samanthula, B. K., & Jiang, W. (2015). Secure multiset intersection cardinality and its application to jaccard coefficient. IEEE Transactions on Dependable,13(5), 591–604.CrossRefGoogle Scholar
  31. Shi, B., Ifrim, G., & Hurley, N. (2016). Learning-to-rank for real-time high-precision hashtag recommendation for streaming news. In Paper presented at the proceedings of the 25th international conference on World Wide Web (pp. 1191–1202). International World Wide Web Conferences Steering Committee.Google Scholar
  32. Song, R., Xu, H., & Cai, L. (2019). Academic collaboration in entrepreneurship research from 2009 to 2018: A multilevel collaboration network analysis. Sustainability,11(19), 5172.  https://doi.org/10.3390/su11195172.CrossRefGoogle Scholar
  33. Sun, Y., & Han, J. (2013). Meta-path-based search and mining in heterogeneous information networks. Tsinghua Science Technology,18(4), 329–338.CrossRefGoogle Scholar
  34. Sun, N., Lu, Y., & Cao, Y. (2019). Career age-aware scientific collaborator recommendation in scholarly big data. IEEE Access,7, 136036–136045.CrossRefGoogle Scholar
  35. Symeonidis, P., & Perentis, C. (2014). Link prediction in multi-modal social networks. In Paper presented at the joint European conference on machine learning and knowledge discovery in databases (pp. 147–162). Springer.Google Scholar
  36. Valdeolivas, A., Tichit, L., Navarro, C., Perrin, S., Odelin, G., Levy, N., et al. (2018). Random walk with restart on multiplex and heterogeneous biological networks. Bioinformatics,35(3), 497–505.CrossRefGoogle Scholar
  37. Wang, X., Fang, Z., & Sun, X. (2016). Usage patterns of scholarly articles on web of science: A study on web of science usage count. Scientometrics,109(2), 917–926.CrossRefGoogle Scholar
  38. Weaver, I. S. (2015). Preferential attachment in randomly grown networks. Physica A: Statistical Mechanics its Applications,439, 85–92.MathSciNetzbMATHCrossRefGoogle Scholar
  39. Wu, J., Zhang, G., & Ren, Y. (2017). A balanced modularity maximization link prediction model in social networks. Information Processing Management,53(1), 295–307.CrossRefGoogle Scholar
  40. 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
  41. Xiao, Y., Li, X., Wang, H., Xu, M., & Liu, Y. (2018). 3-HBP: A three-level hidden Bayesian link prediction model in social networks. IEEE Transactions on Computational Social Systems,5(2), 430–443.CrossRefGoogle Scholar
  42. Xie, Z., Ouyang, Z., Li, J., Dong, E., & Yi, D. (2018). Modelling transition phenomena of scientific coauthorship networks. Journal of the Association for Information Science Technology,69(2), 305–317.CrossRefGoogle Scholar
  43. Yan, E., & Guns, R. (2014). Predicting and recommending collaborations: An author-, institution-, and country-level analysis. Journal of Informetrics,8(2), 295–309.CrossRefGoogle Scholar
  44. Yao, L., Wang, L., Pan, L., & Yao, K. (2016). Link prediction based on common-neighbors for dynamic social network. Procedia Computer Science,83, 82–89.CrossRefGoogle Scholar
  45. Zahr, N., Arnaud, L., Marquet, P., Haroche, J., Costedoat-Chalumeau, N., Hulot, J. S., et al. (2010). Mycophenolic acid area under the curve correlates with disease activity in lupus patients treated with mycophenolate mofetil. Arthritis Rheumatism,62(7), 2047–2054.Google Scholar
  46. Zarrinkalam, F., Kahani, M., & Bagheri, E. (2018). Mining user interests over active topics on social networks. Information Processing Management,54(2), 339–357.CrossRefGoogle Scholar
  47. Zhang, J. (2017). Uncovering mechanisms of co-authorship evolution by multirelations-based link prediction. Information Processing Management,53(1), 42–51.CrossRefGoogle Scholar
  48. Zhao, T., Xiao, R., Sun, C., Chen, H., Li, Y., & Li, H. (2014). Personalized recommendation algorithm integrating roulette walk and combined time effect. Journal of Computer Applications,34(4), 1114–1117.Google Scholar
  49. Zhou, X., Ding, L., Li, Z., & Wan, R. (2017). Collaborator recommendation in heterogeneous bibliographic networks using random walks. Information Retrieval Journal,20(4), 317–337.CrossRefGoogle Scholar
  50. Zhou, T., Lü, L., & Zhang, Y.-C. (2009). Predicting missing links via local information. The European Physical Journal B,71(4), 623–630.zbMATHCrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2020

Authors and Affiliations

  1. 1.College of ManagementShenzhen UniversityShenzhenPeople’s Republic of China
  2. 2.School of Information ManagementNanjing UniversityQixia District, NanjingPeople’s Republic of China
  3. 3.School of ManagementXi’an Jiaotong UniversityXi’anPeople’s Republic of China

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