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Link Prediction Using Evolutionary Neural Network Models

  • Rawan I. Yaghi
  • Hossam Faris
  • Ibrahim Aljarah
  • Ala’ M. Al-Zoubi
  • Ali Asghar Heidari
  • Seyedali MirjaliliEmail author
Chapter
Part of the Algorithms for Intelligent Systems book series (AIS)

Abstract

Link prediction aims to represent the dynamic networks’ relationships of the real world in a model for predicting future links or relationships. This model can help in understanding the evolution of interactions and relationships between network members. Many applications use link prediction such as recommendation systems. Most of the existing link prediction algorithms are based on similarity measures, such as common neighbors and the Adamic/Adar index. The main disadvantage of these algorithms is the low accuracy of results since they depend on the application domain. Moreover, the datasets of link prediction have two significant problems: the imbalanced class distribution and the large size of the data. In this chapter, evolutionary neural network-based models are developed to solve this problem. Three optimizers are used for training feedforward neural network models including genetic algorithm, particle swarm optimization, and moth search. For this purpose, the link prediction problem is formulated as a classification problem to improve the accuracy of the results by constructing features of the traditional link prediction methods and centrality measures in any given link prediction dataset. Also, this work tries to address two problems of the data in two ways: externally using sampling techniques (random and undersampling) and internally using the geometric mean as a fitness function in the proposed algorithms. The results reveal that the proposed model is superior in terms of the sensitivity and geometric mean measures compared to the traditional classifiers and traditional link prediction algorithms.

Keywords

Algorithm Optimization Neural networks Artificial intelligence Machine learning Data science 

References

  1. 1.
    Adamic LA, Adar E (2003) Friends and neighbors on the web. Soc Netw 25(3):211–230CrossRefGoogle Scholar
  2. 2.
    Al Hasan M, Chaoji V, Salem S, Zaki M (2006) Link prediction using supervised learning. In: SDM06: workshop on link analysis, counter-terrorism and securityGoogle Scholar
  3. 3.
    Ala’M A-Z, Faris H et al (2017) Spam profile detection in social networks based on public features. In: 2017 8th International conference on information and communication systems (ICICS). IEEE, pp 130–135Google Scholar
  4. 4.
    Ala’M A-Z, Faris H, Hassonah MA et al (2018) Evolving support vector machines using whale optimization algorithm for spam profiles detection on online social networks in different lingual contexts. Knowl-Based Syst 153:91–104Google Scholar
  5. 5.
    Ala’M A-Z, Rodan A, Alazzam A (2018) Classification model for credit data. In: 2018 Fifth HCT information technology trends (ITT). IEEE, pp 132–137Google Scholar
  6. 6.
    Alian S, Ghatasheh N et al (2014) Multi-agent swarm spreading approach in unknown environments. Int J Comput Sci Issues (IJCSI) 11(2):160Google Scholar
  7. 7.
    Azzini A, Tettamanzi AGB (2011) Evolutionary ANNs: a state of the art survey. Intelligenza Artificiale 5(1):19–35Google Scholar
  8. 8.
    Barabási A-L, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512MathSciNetCrossRefGoogle Scholar
  9. 9.
    Chawla NV (2009) Data mining for imbalanced datasets: an overview. In: Data mining and knowledge discovery handbook. Springer, pp 875–886Google Scholar
  10. 10.
    Chen B, Chen L (2014) A link prediction algorithm based on ant colony optimization. Appl Intell 41(3):694–708CrossRefGoogle Scholar
  11. 11.
    Chen H, Li X, Huang Z (2005) Link prediction approach to collaborative filtering. In Proceedings of the 5th ACM/IEEE-CS joint conference on digital libraries, 2005. JCDL’05. IEEE, pp 141–142Google Scholar
  12. 12.
    Chowdhury GG (2010) Introduction to modern information retrieval. Facet PublishingGoogle Scholar
  13. 13.
    Clauset A, Moore C, Newman MEJ (2008). Hierarchical structure and the prediction of missing links in networks. Nature 453(7191):98CrossRefGoogle Scholar
  14. 14.
    Davis L (1991) Handbook of genetic algorithms. CUMINCADGoogle Scholar
  15. 15.
    Ding S, Su C, Yu J (2011) An optimizing bp neural network algorithm based on genetic algorithm. Artif Intell Rev 36(2):153–162CrossRefGoogle Scholar
  16. 16.
    Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the sixth international symposium on micro machine and human science. IEEE, pp 39–43Google Scholar
  17. 17.
    Esslimani I, Brun A, Boyer A (2011) Densifying a behavioral recommender system by social networks link prediction methods. Soc Netw Anal Mining 1(3):159–172CrossRefGoogle Scholar
  18. 18.
    Faris H, Aljarah I et al (2015) Optimizing feedforward neural networks using krill herd algorithm for e-mail spam detection. In: 2015 IEEE Jordan conference on applied electrical engineering and computing technologies (AEECT). IEEE, pp 1–5Google Scholar
  19. 19.
    Gao F, Musial K, Cooper C, Tsoka S (2015) Link prediction methods and their accuracy for different social networks and network metrics. Sci Program 2015:1Google Scholar
  20. 20.
    Gilbert E, Karahalios K (2009) Predicting tie strength with social media. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 211–220Google Scholar
  21. 21.
    Golberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, ReadingGoogle Scholar
  22. 22.
    Gupta JND, Sexton RS (1999). Comparing backpropagation with a genetic algorithm for neural network training. Omega 27(6):679–684CrossRefGoogle Scholar
  23. 23.
    Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor 11(1):10–18CrossRefGoogle Scholar
  24. 24.
    Ismail AT, Sheta A, Al-Weshah M (2008) A mobile robot path planning using genetic algorithm in static environment. J Comput Sci 4(4):341–344Google Scholar
  25. 25.
    Jaccard P (1901) Étude comparative de la distribution florale dans une portion des alpes et des jura. Bull Soc Vaudoise Sci Nat 37:547–579Google Scholar
  26. 26.
    Kennedy J (1997) The particle swarm: social adaptation of knowledge. In: IEEE International conference on evolutionary computation, 1997. IEEE, pp 303–308Google Scholar
  27. 27.
    Kennedy J (2011) Particle swarm optimization. In: Encyclopedia of machine learning. Springer, pp 760–766Google Scholar
  28. 28.
    Kubat M, Holte RC, Matwin S (1998). Machine learning for the detection of oil spills in satellite radar images. Mach Learn 30(2-3):195–215Google Scholar
  29. 29.
    Kuo T-T, Yan R, Huang Y-Y, Kung P-H, Lin S-D (2013). Unsupervised link prediction using aggregative statistics on heterogeneous social networks. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 775–783Google Scholar
  30. 30.
    Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031CrossRefGoogle Scholar
  31. 31.
    Lichtenwalter RN, Lussier JT, Chawla NV (2010) New perspectives and methods in link prediction. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 243–252Google Scholar
  32. 32.
    Liu H, Hu Z, Haddadi H, Tian H (2013) Hidden link prediction based on node centrality and weak ties. EPL (Europhys Lett) 101(1):18004CrossRefGoogle Scholar
  33. 33.
    Lorrain F, White HC (1971) Structural equivalence of individuals in social networks. J Math Soc 1(1):49–80CrossRefGoogle Scholar
  34. 34.
    Madain A, Ala’M A-Z, Al-Sayyed R et al (2017) Online social networks security: Threats, attacks, and future directions. In: Social media shaping e-publishing and academia. Springer, pp 121–132Google Scholar
  35. 35.
    Murata T, Moriyasu S (2007) Link prediction of social networks based on weighted proximity measures. In: Proceedings of the IEEE/WIC/ACM international conference on web intelligence. IEEE Computer Society, pp 85–88Google Scholar
  36. 36.
    Patro S, Sahu KK (2015) Normalization: a preprocessing stage. arXiv preprint arXiv:1503.06462
  37. 37.
    Quinlan R (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers, San Mateo, CA (1993)Google Scholar
  38. 38.
    Redner S (2008) Networks: teasing out the missing links. Nature 453(7191):47CrossRefGoogle Scholar
  39. 39.
    Rodan A, Faris H et al (2016) Optimizing feedforward neural networks using biogeography based optimization for e-mail spam identification. Int J Commun Netw Syst Sci 9(1):19–28Google Scholar
  40. 40.
    Schall D (2014) Link prediction in directed social networks. Soc Netw Anal Mining 4(1):157CrossRefGoogle Scholar
  41. 41.
    Schifanella R, Barrat A, Cattuto C, Markines B, Menczer F (2010) Folks in folksonomies: social link prediction from shared metadata. In: Proceedings of the third ACM international conference on Web search and data mining. ACM, pp 271–280Google Scholar
  42. 42.
    Shibata N, Kajikawa Y, Sakata I (2012) Link prediction in citation networks. J Am Soc Inf Sci Technol 63(1):78–85CrossRefGoogle Scholar
  43. 43.
    Taskar B, Wong M-F, Abbeel P, Koller D (2004) Link prediction in relational data. In: Advances in neural information processing systems, pp 659–666Google Scholar
  44. 44.
    Wang C, Satuluri V, Parthasarathy S (2007) Local probabilistic models for link prediction. In: Seventh IEEE international conference on data mining (ICDM 2007). IEEE, pp 322–331Google Scholar
  45. 45.
    Wang G-G (2016) Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems. Memetic Comput 1–14Google Scholar
  46. 46.
    Xie Z (2005) Centrality measures in text mining: prediction of noun phrases that appear in abstracts. In :Proceedings of the ACL student research workshop. Association for Computational Linguistics, pp 103–108Google Scholar
  47. 47.
    Yamany W, Fawzy M, Tharwat A, Hassanien AE (2015) Moth-flame optimization for training multi-layer perceptrons. In: 2015 11th International computer engineering conference (ICENCO). IEEE, pp 267–272Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Rawan I. Yaghi
    • 1
  • Hossam Faris
    • 1
  • Ibrahim Aljarah
    • 1
  • Ala’ M. Al-Zoubi
    • 1
  • Ali Asghar Heidari
    • 2
    • 3
  • Seyedali Mirjalili
    • 4
    • 5
    Email author
  1. 1.King Abdullah II School for Information TechnologyThe University of JordanAmmanJordan
  2. 2.School of Surveying and Geospatial Engineering, College of EngineeringUniversity of TehranTehranIran
  3. 3.Department of Computer Science, School of ComputingNational University of SingaporeSingaporeSingapore
  4. 4.Torrens University AustraliaBrisbaneAustralia
  5. 5.Griffith UniversityBrisbaneAustralia

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