Abstract
The link prediction problem has received much attention since the beginnings of social and behavioral sciences. For instance, social networks such as Facebook, Twitter, and LinkendIN change enduringly as new connections appear in the graph. For these networks, one of the biggest challenges is to find accurately the best recommendations to the users. Within the meaning of the graph, the main objective of the link prediction problem is to predict the upcoming links from the actual state of a graph. Link prediction methods use some score functions, such as Jaccard coefficient, Katz index, and Adamic Adar metric, to measure the probability of adding the links to the network. These metrics are widely used in various applications due to their simplicity and their interpretability; however, the majority of them are designed for a specific domain. Social networks become very large with a several number of users that are connected with different kinds of links. Predicting those links is still a challenging task, as we need to find the best way to perform predictions as accurate as possible. Along this way, we extend our previous work is (Jibouni et al. in 2018 6th international conference on wireless networks and mobile communications (WINCOM). IEEE, pp 1–6, 2018) where we have proposed a new node similarity measure based on the path depth between the source and destination nodes and their degrees. The used topological features are very easy to compute and very effective in solving the link prediction problem. In addition, we verify the impact of the path length l on the method performance and we show that the proposed method provides more accurate recommendations by using the path length 2 and 3. Then, we compare 13 state-of-the-art methods against the proposed method in terms of their prediction performance using the area under curve. The results on five instances of social networks show the efficiency of the proposed method in providing accurate recommendations. Furthermore, we consider machine learning techniques such as K-nearest neighbors, logistic regression, artificial neural network, decision tree, random forest, support vector machine to solve the link prediction problem as a binary classification task. The results confirm the significant accuracy improvement that can be achieved using the proposed metric.
Similar content being viewed by others
References
Adamic LA, Adar E (2003) Friends and neighbors on the web. Soc Netw 25(3):211–230
Al Hasan M, Chaoji V, Salem S, Zaki M (2006) Link prediction using supervised learning. In SDM06: workshop on link analysis, counter-terrorism and security
Barabâsi AL, Jeong H, Néda Z, Ravasz E, Schubert A, Vicsek T (2002) Evolution of the social network of scientific collaborations. Physica A Stat Mech Its Appl 311(3–4):590–614
Esslimani I, Brun A, Boyer A (2011) Densifying a behavioral recommender system by social networks link prediction methods. Soc Netw Anal Min 1(3):159–172
Folino F, Pizzuti C (2012) Link prediction approaches for disease networks. In: International conference on information technology in bio-and medical informatics. Springer, Berlin, pp 99–108
Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826
Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36
Huang Z, Li X, Chen H (2005) Link prediction approach to collaborative filtering. In: ACM/IEEE joint conference on digital libraries, JCDL 2005, Proceedings. Denver, CO, USA, 7-11, pp 141-142
Jaccard P (1901) Etude comparative de la distribution florale dans une portion des Alpes et des Jura. Bull Soc Vaud Sci Nat 37:547–579
Jibouni A, Lotfi D, El Marraki M, Hammouch A (2018) A novel parameter free approach for link prediction. In: 2018 6th international conference on wireless networks and mobile communications (WINCOM). IEEE, pp 1–6
Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18(1):39–43
Klimt B, Yang Y (2004) Introducing the Enron corpus. In: CEAS
KONECT (2017) Us power grid network dataset. April 2017
Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87(19):198701
Leicht EA, Holme P, Newman ME (2006) Vertex similarity in networks. Phys Rev E 73(2):026120
Leskovec J, Mcauley JJ (2012) Learning to discover social circles in ego networks. In: Advances in neural information processing systems, pp 539–547
Leskovec J, Lang KJ, Dasgupta A, Mahoney MW (2009) Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math 6(1):29–123
Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031
Lü L, Zhou T (2011) Link prediction in complex networks: a survey. Physica A Stat Mech Its Appl 390(6):1150–1170
Lu L, Jin CH, Zhou T (2009) Similarity index based on local paths for link prediction of complex networks. Phys Rev E 80(4):046122
Lusseau D, Schneider K, Boisseau OJ, Haase P, Slooten E, Dawson SM (2003) The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations. Behav Ecol Sociobiol 54(4):396–405
Martínez V, Berzal F, Cubero JC (2017) A survey of link prediction in complex networks. ACM Comput Surv (CSUR) 49(4):69
Newman ME (2001) Clustering and preferential attachment in growing networks. Phys Rev E 64(2):025102
Newman ME (2002) Assortative mixing in networks. Phys Rev Lett 89(20):208701
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Vanderplas J (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabási AL (2002) Hierarchical organization of modularity in metabolic networks. Science 297(5586):1551–1555
Salton G, McGill MJ (1986) Introduction to modern information retrieval. p 400
Shao C, Xu Y (2016, May) Data exchange similarity based on flow field for link prediction problem. In: 2016 sixth international conference on information science and technology (ICIST). IEEE, pp 84–89
Snijders TA (1981) The degree variance: an index of graph heterogeneity. Soc Netw 3(3):163–174
Sorensen TA (1948) A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biol Skar 5:1–34
Wang P, Xu B, Wu Y, Zhou X (2015) Link prediction in social networks: the state-of-the-art. Sci China Inf Sci 58(1):1–38
Watts DJ, Strogatz SH (1998) The dynamics of networks between order and randomness. Nature 393:440
Watts Duncan J, Strogatz Steven H (1998) Collective dynamics of ‘small-world’ networks. Nature 393(1):440–442
Zhou T, Lu L, Zhang YC (2009) Predicting missing links via local information. Eur Phys J B 71(4):623–630
Zhu YX, Lu L, Zhang QM, Zhou T (2012) Uncovering missing links with cold ends. Physica A Stat Mech Its Appl 391(22):5769–5778
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
Rights and permissions
About this article
Cite this article
Ayoub, J., Lotfi, D., El Marraki, M. et al. Accurate link prediction method based on path length between a pair of unlinked nodes and their degree. Soc. Netw. Anal. Min. 10, 9 (2020). https://doi.org/10.1007/s13278-019-0618-2
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13278-019-0618-2