COMMON NEIGHBORHOOD-BASED LINK PREDICTION IN SPORTS NETWORKS

Yıl 2023, Cilt: 1 Sayı: 1, 10 - 21, 02.02.2024

Emrah Özkaynak Mine Keleş

Öz

Link prediction has been among the popular topics in social network analysis studies in recent years. The prediction of new links that may arise in the future, depending on the analysis of the relations between the entities, has started to be used frequently, especially in recommendation systems. Link prediction methods, especially used in social networks, mostly use the topological features of complex networks in terms of application. This situation has also paved the way for link prediction methods to be preferred in almost all kinds of networks of complex network structures. The increased trend in link prediction studies has also allowed many methods to be proposed and used in this field. The differences in the formation of the network and the link types prevent the developed methods from giving the same performance for every complex network. This situation has increased the importance of choosing the appropriate link prediction method depending on the structure of the complex network. This study applied neighborhood-based link prediction methods in networks created from different sports competitions. Furthermore, The most suitable neighborhood-based link prediction method that could be used in sports networks has been investigated. Link prediction methods were applied to the networks formed with different time periods formed from different sports branches such as tennis tournaments, football competitions, and billiards competitions, and the accuracy performances of the methods were determined. The results obtained from the AUC metric in the experimental studies show that the neighborhood-based link prediction methods successfully predict the new connections that may arise in the future in sports networks.

Anahtar Kelimeler

Link Prediction, Social Networks, Data Mining.

Kaynakça

• L. Getoor and C. P. Diehl, “Link Mining: A Survey.”
• A. Kumar, S. Mishra, S. S. Singh, K. Singh, and B. Biswas, “Link prediction in complex networks based on Significance of Higher-Order Path Index (SHOPI),” Physica A: Statistical Mechanics and its Applications, vol. 545, May 2020, doi: 10.1016/j.physa.2019.123790.
• P. Srilatha and R. Manjula, “Similarity Index based Link Prediction Algorithms in Social Networks: A Survey,” 2016.
• S. H. Strogatz, “Exploring complex networks.,” Nature, vol. 410, pp. 268–276, 2001.
• Newman, M. E. J.:Siam Rev. 45, 167 (2003).
• Dorogovtsev ,S. N.,Mendes ,J. F. F.: In Evolution Of Networks, Oxford University Press, Oxford, (2003).
• Dodds ,P. S., Muhamad ,R., Watts ,D. J.: Science 301, 827 (2003).
• Watts ,D. J., S. Strogatz,H.: Nature (London) 393, 440 (1998).
• Watts Dj, Strogatz Sh: Collective Dynamics Of 'Small-World' Networks. Nature. 393 (6684): 440–2, (1998).
• Erdos, P., Rényi, A, On Random Graphs. I(Pdf). Publicationes Mathematicae. 6: 290–297, (1959).
• Barabasi ,A. -L., Albert ,R.: Emergence Of Scaling In Random Networks, Science, 286, 509 (1999).
• Newman M.E.J.: Clustering And Preferential Attachment In Growing Networks, Phys. Rev. E - Stat. Physics, Plasmas, Fluids, Relat. Interdiscip. Top. 64 (2001).
• Jaccard P., Etude De La Distribution Florale Dans Une Portion Des Alpes Et Du Jura, Bull. La Soc. Vaudoise Des Sci. Nat. 37, 547–579,(1901)
• Barabasi Albert-Laszlo, Albert Reka, Emergence Of Scaling In Random Networks, Science (80-. ). 286 (1999) 509–512.
• Barabasi ,A. -L., Albert ,R.: Statistical Mechanics Of Complex Networks, Reviews Of Modern Physics. 74 (1): 47–97,(2002)
• Zhou,T., Lü, L., Zhang, Y.C.: Predicting Missing Links Via Local Information, Eur. Phys. J. B. 71 623–630, (2009)
• Sorensen T.: A Method Of Establishing Groups Of Equal Amplitude In Plant Socio-logy Based On Similarity Of Species Content., Det. Kong. Danske Vidensk, Selesk Biol. Skr. 5,1–34, (1948) .
• Liben-Nowell, D., Kleinberg,J.: The Link-Prediction Problem For Social Networks, J. Am. Soc. Inf. Sci. Technol. 58, 1019–1031, (2007).
• Linyuan, L.L., Zhou, T.: Link Prediction İn Complex Networks: A Survey, Phys. A Stat. Mech. Its Appl. 390, 1150–1170, (2011)
• Huang, Z.: Link Prediction Based On Graph Topology: The Predictive Value Of Gene-ralized Clustering Coefficient, Ssrn. (2010).
• Resnick, P., Varian, H.R.: Recommender Systems Mmende Tems, Commun. Acm. 40, 56–58, (1997).
• Lü, L., Medo, M., Yeung, C.H., Zhang, Y.-C., Zhang, Z.-K., Zhou, T.: Recommender Systems, Phys. Rep. 519, 1–49, (2012).
• Huang, Z., Li, X., Chen, H.: Link Prediction Approach To Collaborative Filtering, Proc. 5th Acm/Ieee-Cs Jt. Conf. Digit. Libr. - Jcdl ’05. (2005)
• Kleinberg, J.: Analysis Of Large-Scale Social And İnformation Networks Subject Areas , Author For Correspondence, (2013).
• Zhang, Q.M., Lü, L., Wang, W.Q., Zhu, Y.X., Zhou, T.: Potential Theory For Directed Networks, Plos One. 8 (2013)
• Bürhan, Y., Daş, R.: Akademik Veritabanlarından Yazar-Makale Bağlantı Tahmini, Politeknik Dergisi Journal Of Polytechnic, 787-800, Ankara(2017).
• 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).
• Wang, W.Q., Zhang, Q.M., Zhou, T., Evaluating Network Models: A Likelihood Analysis, Epl. 98, 1–6, (2012)
• Joseph D O’Brien, James P Gleeson, A complex networks approach to ranking professional Snooker players, Journal of Complex Networks, Volume 8, Issue 6, 1 December 2020
• Fındık ,O., Özkaynak ,E.: Complex Network Analysis Of Players In Tennis Tourna-ments, International Conference On Advanced Technologies, Computer Engineering And Science (Icatces’18), 383-388, Karabük (2018) Sulak ,E.E., Yılmaz,H. Özkaynak,E.: Complex Network Analysis Of Uefa Europe Lea-gue Competitions, International Conference On Advanced Technologies, Computer Engineering And Science (Icatces’18), 389-393, Karabük (2018)
• Hanley, J. A. and McNeil, B. J., "The meaning and use of the area under a receiver operating characteristic (ROC) curve.", Radiology, 143 (1): 29–36 (1982).
• H. Wang and Z. Le, “Seven-layer model in complex networks link prediction: A survey,” Sensors (Switzerland), vol. 20, no. 22. MDPI AG, pp. 1–33, Nov. 02, 2020. doi: 10.3390/s20226560.
• Newman M.E.J.: Clustering and Preferential Attachment in Growing Networks, Phys. Rev. E - Stat. Physics, Plasmas, Fluids, Relat. Interdiscip. Top. 64 (2001).
• Murata, T. and Moriyasu, S., "Link Prediction of Social Networks Based on Weighted Proximity Measures", IEEE/WIC/ACM International Conference on Web Intelligence (WI'07), Fremont, 85–88 (2007).
• Jaccard, P., "Etude de la distribution florale dans une portion des Alpes et du Jura", Bulletin De La Societe Vaudoise Des Sciences Naturelles, 37: 547–579 (1901).
• De Sa, H. R. and Prudencio, R. B. C., "Supervised link prediction in weighted networks", The 2011 International Joint Conference on Neural Networks, San Jose, 2281–2288 (2011). Adamic, L. A. and Adar, E., "Friends and neighbors on the Web", Social Networks, 25 (3): 211–230 (2003).
• Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B 71, 623–630 (2009)
• www.figshare.com/articles/dataset/Snooker\_datasets/13604480/1
• www.tennis-data.co.uk/alldata.php
• www.Kassiesa. Ho-me.Xs4all.Nl/Bert/Uefa/Data/Index.Html (2018).