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An evolutionary non-linear ranking algorithm for ranking scientific collaborations

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Abstract

The social capital theory motivates some researchers to apply link-based ranking algorithms (e.g. PageRank) to compute the fitness level of a scholar for collaborating with other scholars on a set of skills. These algorithms are executed on the collaboration network of scholars and assign a score to each scholar based on the scores of his/her neighbors by solving a linear system in an iterative way. In this paper, we propose a new ranking algorithm by focusing on link-aggregation function and transition matrix. The evolution strategy technique is applied to find the best aggregation function and transition matrix for computing the score of a scholar in the collaboration network which is modeled by a hypergraph. Experiments conducted on two datasets gathered from ScivalExpert and VIVO show that the new non-linear ranking algorithm acts better than the other iterative ranking approaches for ranking scientific collaborations.

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Notes

  1. http://www.vivoweb.org/

  2. http://connects.catalyst.harvard.edu/Profiles/search

  3. http://www.biomedexperts.com/

  4. http://www.epernicus.com

  5. http://www.researchcrossroads.com/

  6. https://www.elsevier.com/solutions/scival

  7. A unique identifier (uid) assigned by Scival Expert and VIVO to each scholar.

  8. http://vivo.scholars.northwestern.edu/

  9. https://sparql.scholars.duke.edu/

References

  1. Abbasi A, Altmann J, Hossain L (2011) Identifying the effects of co-authorship networks on the performance of scholars: a correlation and regression analysis of performance measures and social network analysis measures. Nat Comput 5(4):594–607

    Google Scholar 

  2. Abbasi A, Wigand RT, Hossain L Measuring social capital through network analysis and its influence on individual performance

  3. Anyanwu K, Maduko A, Sheth A (2005) Semrank: ranking complex relationship search results on the semantic web. In: Proceedings of the 14th international conference on world wide web. ACM, pp 117–127

  4. Bäck T, Emmerich M (2002) Evolution strategies for optimisation in engineering applications. In: Proceedings 5th world congress on computational mechanics. Citeseer

  5. Badar K, Hite JM, Badir YF (2013) Examining the relationship of co-authorship network centrality and gender on academic research performance: the case of chemistry researchers in Pakistan. Scientometrics 94(2):755–775

    Article  Google Scholar 

  6. Beyer HG, Schwefel HP (2002) Evolution strategies–a comprehensive introduction. Nat Comput 1(1):3–52

    Article  MathSciNet  MATH  Google Scholar 

  7. Bin L, Shuming S, Yunxiao M, Ji-Rong W (2009) Nonlinear algorithms for static-rank computation Technical Report

  8. Börner K, Conlon M, Corson-Rikert J, Ding Y (2012) Vivo: a semantic approach to scholarly networking and discovery. Synthesis lectures on the Semantic Web: theory and technology 7(1):1–178

    Google Scholar 

  9. Chang YC, Zeyar A (2015) Authorrank: a new scheme for identifying field-specific key researchers. In: Proceedings 8th international conference on information resources management (conf-IRM), pp P01:1–13

  10. Cohen J (1992) A power primer. Psychol Bull 112(1):155

    Article  Google Scholar 

  11. Cooke N, Hilton M (2015) Enhancing the effectiveness of team science washington (DC): National academies press (US)

  12. Ding L, Finin T, Joshi A, Pan R, Cost RS, Peng Y, Reddivari P, Doshi V, Sachs J (2004) Swoogle: a search and metadata engine for the semantic web. In: Proceedings of the thirteenth ACM international conference on information and knowledge management. ACM, pp 652–659

  13. Eslami H, Ebadi A, Schiffauerova A (2013) Effect of collaboration network structure on knowledge creation and technological performance: the case of biotechnology in Canada. Scientometrics 97(1):99–119

    Article  Google Scholar 

  14. Ghasemian F, Zamanifar K, Ghasem-Aqaee N, Contractor N (2016) Toward a better scientific collaboration success prediction model through the feature space expansion. Scientometrics: 1–25

  15. Gholamipoor M, Ghadimi P, Alavidoost MH, Feizi Chekab MA (2014) Application of evolution strategy algorithm for optimization of a single-layer sound absorber. Cogent Engineering 1(1):945–820

    Article  Google Scholar 

  16. Gleich DF (2015) Pagerank beyond the web. SIAM Rev 57(3):321–363

    Article  MathSciNet  MATH  Google Scholar 

  17. Haveliwala TH (2002) Topic-sensitive pagerank. In: Proceedings of the 11th international conference on world wide web. ACM, pp 517–526

  18. Herrero JG, Portas JAB, De Jesús AB, López JMM, de Miguel Vela G, Corredera JRC (2003) Application of evolution strategies to the design of tracking filters with a large number of specifications. EURASIP Journal on Applied Signal Processing 2003:766–779

    MATH  Google Scholar 

  19. Hogan A, Decker S, Harth A (2006) Reconrank: A scalable ranking method for semantic web data with context

  20. Hu W, Zou H, Gong Z (2015) Temporal pagerank on social networks. In: International conference on web information systems engineering. Springer, pp 262–276

  21. Jing Y, Baluja S (2008) Visualrank: Applying pagerank to large-scale image search. IEEE Trans Pattern Anal Mach Intell 30(11):1877–1890

    Article  Google Scholar 

  22. Kamvar S, Haveliwala T, Manning C, Golub G (2003) Exploiting the block structure of the web for computing pagerank Stanford University Technical Report

  23. Kamvar SD, Haveliwala TH, Manning CD, Golub GH (2003) Extrapolation methods for accelerating pagerank computations. In: Proceedings of the 12th international conference on world wide web. ACM, pp 261–270

  24. Kim KS, Choi YS (2015) Incremental iteration method for fast pagerank computation. In: Proceedings of the 9th international conference on ubiquitous information management and communication. ACM, p 80

  25. Kleinberg JM (1999) Hubs, authorities, and communities. ACM Comput Surv (CSUR) 31(4es):5

    Article  Google Scholar 

  26. Koumoutsakos P, Freund J, Parekh D (1998) Evolution strategies for parameter optimization in jet flow control. In: Proceedings of the summer program, pp 121–132

  27. Lappas T, Liu K, Terzi E (2009) Finding a team of experts in social networks. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 467–476

  28. Li EY, Liao CH, Yen HR (2013) Co-authorship networks and research impact: a social capital perspective. Res Policy 42(9):1515–1530

    Article  Google Scholar 

  29. Liao CH (2011) How to improve research quality? examining the impacts of collaboration intensity and member diversity in collaboration networks. Scientometrics 86(3):747–761

    Article  Google Scholar 

  30. Mbikayi HK (2013) Toward evolution strategies application in automatic polyphonic music transcription using electronic synthesis. arXiv:1304.0969

  31. Miguez E, Diaz-Dorado E, Cidras J (1998) An application of an evolution strategy in power distribution system planning. In: IEEE world congress on computational intelligence., the 1998 IEEE international conference on Evolutionary computation proceedings, 1998. IEEE, pp 241–246

  32. Mika P, Elfring T, Groenewegen P (2006) Application of semantic technology for social network analysis in the sciences. Scientometrics 68(1):3–27

    Article  Google Scholar 

  33. Nie Z, Zhang Y, Wen JR, Ma WY (2005) Object-level ranking: bringing order to web objects. In: Proceedings of the 14th international conference on world wide web. ACM, pp 567–574

  34. Nykl M, Campr M, Ježek K (2015) Author ranking based on personalized pagerank. J Informet 9 (4):777–799

    Article  Google Scholar 

  35. Page L, Brin S, Motwani R, Winograd T (1999) The pagerank citation ranking: Bringing order to the web. Tech. rep., Stanford InfoLab

  36. Roa-Valverde AJ, Sicilia MA (2014) A survey of approaches for ranking on the web of data. Inf Retr 17 (4):295–325

    Article  Google Scholar 

  37. Schleyer T, Butler BS, Song M, Spallek H (2012) Conceptualizing and advancing research networking systems. ACM Transactions on Computer-Human Interaction (TOCHI) 19(1):2

    Article  Google Scholar 

  38. Schleyer T, Spallek H, Butler BS, Subramanian S, Weiss D, Poythress ML, Rattanathikun P, Mueller G (2008) Requirements for expertise location systems in biomedical science and the semantic web. In: Proceedings of the 3rd expert finder workshop on personal identification and collaboration: Knowledge mediation and extraction PICKME 2008, pp 31–41

  39. Senanayake U, Piraveenan M, Zomaya A (2015) The pagerank-index: Going beyond citation counts in quantifying scientific impact of researchers. PloS one 10(8):e0134,794

    Article  Google Scholar 

  40. Shir OM, Siedschlag C, Bäck T, Vrakking MJ (2005) Niching in evolution strategies and its application to laser pulse shaping. In: International conference on artificial evolution (evolution artificielle). Springer, pp 85–96

  41. Sonnenwald DH (2007) Scientific collaboration: a synthesis of challenges and strategies. Annual Review of Information Science and Technology p 643681

  42. Stokols D, Hall KL, Taylor BK, Moser RP (2008) The science of team science: overview of the field and introduction to the supplement. Am. J. Prev. Med. 35(2):S77–S89

    Article  Google Scholar 

  43. Tan S, Bu J, Chen C, He X (2011) Using rich social media information for music recommendation via hypergraph model. In: Social media modeling and computing. Springer, pp 213–237

  44. Uddin S, Hossain L, Abbasi A, Rasmussen K (2012) Trend and efficiency analysis of co-authorship network. Scientometrics 90(2):687–699

    Article  Google Scholar 

  45. Wei W (2009) Semantic search: Bringing semantic web technologies to information retrieval. Ph.D. thesis, Ph. D. thesis, University of Nottingham

  46. Wuchty S, Jones BF, Uzzi B (2007) The increasing dominance of teams in production of knowledge. Science 316(5827):1036–1039

    Article  Google Scholar 

  47. Yao L, Wei T, Zeng A, Fan Y, Di Z (2014) Ranking scientific publications: the effect of nonlinearity. Sci Rep 4:6663

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Software Engineering, Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran

    Fahimeh Ghasemian, Kamran Zamanifar & Nasser Ghasem-Aghaee

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  1. Fahimeh Ghasemian
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  2. Kamran Zamanifar
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  3. Nasser Ghasem-Aghaee
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Correspondence to Kamran Zamanifar.

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Ghasemian, F., Zamanifar, K. & Ghasem-Aghaee, N. An evolutionary non-linear ranking algorithm for ranking scientific collaborations. Appl Intell 48, 465–481 (2018). https://doi.org/10.1007/s10489-017-0990-4

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  • DOI: https://doi.org/10.1007/s10489-017-0990-4

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