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Link Prediction Using Evolutionary Neural Network Models

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Evolutionary Machine Learning Techniques

Part of the book series: Algorithms for Intelligent Systems ((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.

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Notes

  1. 1.

    MATLAB code of MS is available at: https://www.mathworks.com/matlabcentral/fileexchange/59010-moth-search-ms-algorithm.

  2. 2.

    Interested readers refer to: (a) igraph: https://igraph.org/r/ (b) ppiPre: https://cran.r-project.org/src/contrib/Archive/ppiPre/.

  3. 3.

    Readers may refer to (a) https://machinelearningmastery.com/how-to-run-your-first-classifier-in-weka/ (b) NEO Group website at http://neo.lcc.uma.es.

  4. 4.

    Please refer to: http://weka.sourceforge.net/doc.dev/weka/classifiers/trees/J48.html.

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Author information

Authors and Affiliations

  1. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Rawan I. Yaghi, Hossam Faris, Ibrahim Aljarah & Ala’ M. Al-Zoubi

  2. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Ali Asghar Heidari

  3. Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore

    Ali Asghar Heidari

  4. Torrens University Australia, Fortitude Valley, Brisbane, QLD, 4006, Australia

    Seyedali Mirjalili

  5. Griffith University, Brisbane, QLD, 4111, Australia

    Seyedali Mirjalili

Authors
  1. Rawan I. Yaghi
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  2. Hossam Faris
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  3. Ibrahim Aljarah
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  4. Ala’ M. Al-Zoubi
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  5. Ali Asghar Heidari
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  6. Seyedali Mirjalili
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Corresponding author

Correspondence to Seyedali Mirjalili .

Editor information

Editors and Affiliations

  1. Torrens University Australia, Brisbane, QLD, Australia

    Seyedali Mirjalili

  2. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Hossam Faris

  3. King Abdullah II School for Information Technology, The University of Jordan, Amman, Jordan

    Ibrahim Aljarah

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Yaghi, R.I., Faris, H., Aljarah, I., Al-Zoubi, A.M., Heidari, A.A., Mirjalili, S. (2020). Link Prediction Using Evolutionary Neural Network Models. In: Mirjalili, S., Faris, H., Aljarah, I. (eds) Evolutionary Machine Learning Techniques. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-32-9990-0_6

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