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  • Link Prediction Using Evolutionary Neural Network Models

    Author(s)
    Yaghi, Rawan I
    Faris, Hossam
    Aljarah, Ibrahim
    Al-Zoubi, Ala’ M
    Heidari, Ali Asghar
    Mirjalili, Seyedali
    Griffith University Author(s)
    Mirjalili, Seyedali
    Year published
    2020
    Metadata
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    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 ...
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    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.
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    Book Title
    Evolutionary Machine Learning Techniques
    DOI
    https://doi.org/10.1007/978-981-32-9990-0_6
    Subject
    Neural networks
    Machine learning
    Artificial intelligence
    Publication URI
    http://hdl.handle.net/10072/398829
    Collection
    • Book chapters

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