Skip to main content
SpringerLink
Log in

Link Prediction on Networks Created from UEFA European Competitions

  • Conference paper
  • First Online:
Cyber Security and Computer Science (ICONCS 2020)

Included in the following conference series:

  • 1164 Accesses

Abstract

Link prediction is widely used in network analysis to identify future links between nodes. Link prediction has an important place in terms of being applicable to many real-world networks with dynamic structure. Networks with dynamic structure, such as social networks, scientific collaboration networks and metabolic networks, are networks in which link prediction studies are performed. In addition, it is seen that there are few studies showing the feasibility of link prediction by creating networks from different areas. In this study, in order to show the applicability of link prediction processes in different fields link prediction was made by applying traditional link prediction methods in the networks formed from the data of football competitions played after the groups between the years 2004–2017 in the UEFA European League. The AUC metric was used to measure the success of forecasting. The results show that link prediction methods can be used in sports networks.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Newman, M.E.J.: SIAM Rev. 45, 167 (2003)

    Article  MathSciNet  Google Scholar 

  2. Dorogovtsev, S.N., Mendes, J.F.: Evolution of Networks. Oxford University Press, Oxford (2003)

    Book  Google Scholar 

  3. Dodds, P.S., Muhamad, R., Watts, D.J.: Science 301, 827 (2003)

    Article  Google Scholar 

  4. Watts, D.J., Strogatz, S.H.: Nature (London) 393, 440 (1998)

    Google Scholar 

  5. Fındık, O., Özkaynak, E.: Complex network analysis of players in tennis tournaments. In: International Conference on Advanced Technologies, Computer Engineering and Science (ICATCES 2018), Karabük, pp. 383–388 (2018)

    Google Scholar 

  6. Sulak, E.E., Yılmaz, H. Özkaynak, E.: Complex network analysis of UEFA Europe league competitions. In: International Conference on Advanced Technologies, Computer Engineering and Science (ICATCES 2018), Karabük, pp. 389–393 (2018)

    Google Scholar 

  7. Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440–442 (1998)

    Article  Google Scholar 

  8. Erdős, P., Rényi, A.: On random graphs. I(Pdf). Publications Mathematica 6, 290–297 (1959)

    Google Scholar 

  9. Barabasi, A.-L., Albert, R.: Emergence of scaling in random networks. Science 286, 509 (1999)

    Article  MathSciNet  Google Scholar 

  10. Scott, J.: Social Network Analysis: A Handbook. Sage Publications, London (2000). 209 p.

    Google Scholar 

  11. Mika, P.: Social networks and the semantic web. In: Jain, R., Sheth, A. (eds.) Semantic Web And Beyond Computing For Human Experience. Springer, New York (2007). https://doi.org/10.1007/978-0-387-71001-3. 234 p.

  12. Newman, M.E.J.: Mathematics of networks. In: Durlauf, S.N., Blume, L.E. (eds.) The New Palgrave Dictionary of Economics. Palgrave Macmillan, London (2008). https://doi.org/10.1007/978-1-349-58802-2_1061

    Chapter  Google Scholar 

  13. Ruhnau, B.: Eigenvector centrality: a node-centrality? Soc. Netw. 22(4), 357–365 (2000)

    Article  Google Scholar 

  14. Newman, M.E.J.: Clustering and preferential attachment in growing networks. Phys. Rev. E Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top 64, 025102 (2001)

    Google Scholar 

  15. Paul: Etude De La Distribution Florale Dans Une Portion Des Alpes Et Du Jura. Bull. La Soc. Vaudoise Des Sci. Nat. 37, 547–579 (1901)

    Google Scholar 

  16. Barabasi, A.-L., Reka, A.: Emergence of scaling in random networks. Science (80−) 286, 509–512 (1999)

    Google Scholar 

  17. Barabasi, A.-L., Albert, R.: Statistical mechanics of complex networks. Rev. Modern Phys. 74(1), 47–97 (2002)

    Article  MathSciNet  Google Scholar 

  18. Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B 71, 623–630 (2009)

    Article  Google Scholar 

  19. Sorensen, T.: A Method of establishing groups of equal amplitude in plant sociology based on similarity of species content. Det. Kong. Danske Vidensk, Selesk Biol. Skr. 5, 1–34 (1948)

    Google Scholar 

  20. Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inf. Sci. Technol. 58, 1019–1031 (2007)

    Article  Google Scholar 

  21. Linyuan, L.L., Zhou, T.: Link prediction in complex networks: a survey. Phys. A Stat. Mech. Appl. 390, 1150–1170 (2011)

    Article  Google Scholar 

  22. Liu, Z., Zhang, Q.M., Lü, L., Zhou, T.: Link prediction in complex networks: a local Naïve Bayes model. EPL 96, 48007 (2011)

    Google Scholar 

  23. Huang, Z.: Link prediction based on graph topology: the predictive value of generalized clustering coefficient. SSRN (2010)

    Google Scholar 

  24. Resnick, P., Varian, H.R.: Recommender systems Mmende Tems. Commun. ACM 40, 56–58 (1997)

    Article  Google Scholar 

  25. Lü, L., Medo, M., Yeung, C.H., Zhang, Y.-C., Zhang, Z.-K., Zhou, T.: Recommender systems. Phys. Rep. 519, 1–49 (2012)

    Article  Google Scholar 

  26. Huang, Z., Li, X., Chen, H.: Link prediction approach to collaborative filtering. In: Proceedings of 5th ACM/IEEE Joint Conference on Digital Libraries, JCDL 2005 (2005)

    Google Scholar 

  27. Kleinberg, J.: Analysis of Large-Scale Social And İnformation Networks Subject Areas. Author for Correspondence (2013)

    Google Scholar 

  28. Zhang, Q.M., Lü, L., Wang, W.Q., Zhu, Y.X., Zhou, T.: Potential theory for directed networks. Plos One 8 e55437 (2013)

    Google Scholar 

  29. Wang, W.Q., Zhang, Q.M., Zhou, T.: Evaluating network models: a likelihood analysis. EPL 98, 1–6 (2012)

    Google Scholar 

  30. Bürhan, Y., Daş, R.: Akademik Veritabanlarından Yazar-Makale Bağlantı Tahmini. Politeknik Dergisi J. Polytech. 20 787–800 (2017)

    Google Scholar 

  31. Türker, İ., Çavuşoğlu, A.: Detailing the co-authorship networks in degree coupling, edge weight and academic age perspective. Chaos Solitons Fractals 91, 386–392 (2016)

    Article  Google Scholar 

  32. Hanley, J.A., Mcneil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–36 (1982)

    Article  Google Scholar 

  33. Bastian, M., Heymann, S., Jacom, M.: Gephi: an open source software for exploring and manipulating networks. In: Third International AAAI Conference on Weblogs and Social Media. AAAI Publications (2009)

    Google Scholar 

  34. Internet: UEFA European Cup Coefficients Database (2018). https://Kassiesa.Home.Xs4all.Nl/Bert/Uefa/Data/Index.Html

Download references

Author information

Authors and Affiliations

  1. Karabük University, Karabük, Turkey

    Oğuz Findik & Emrah Özkaynak

Authors
  1. Oğuz Findik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emrah Özkaynak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oğuz Findik .

Editor information

Editors and Affiliations

  1. Daffodil International University, Dhaka, Bangladesh

    Touhid Bhuiyan

  2. Daffodil International University, Dhaka, Bangladesh

    Md. Mostafijur Rahman

  3. Daffodil International University, Dhaka, Bangladesh

    Md. Asraf Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Findik, O., Özkaynak, E. (2020). Link Prediction on Networks Created from UEFA European Competitions. In: Bhuiyan, T., Rahman, M.M., Ali, M.A. (eds) Cyber Security and Computer Science. ICONCS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 325. Springer, Cham. https://doi.org/10.1007/978-3-030-52856-0_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-52856-0_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-52855-3

  • Online ISBN: 978-3-030-52856-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation