Link Prediction Using Evolutionary Neural Network Models

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


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.


Algorithm Optimization Neural networks Artificial intelligence Machine learning Data science 


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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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