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Deep Convolutional Neural Networks for Feature Extraction of Images Generated from Complex Networks Topologies

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Abstract

To identify topology features of different complex network topology is essential in network science researches. Apart from traditional tools in doing such jobs such as power-law, a proved method of convolutional neural network (CNN) is introduced into this research field after we re-format the complex network topology adjacent matrix into an image. We design a CNN of overall 10 layers comprising convolutional layers, pooling layers and a softmax dense layer at last to extract relevant features and classify such features. Experiments show that the CNN models can effectively extract target features and result in an average accuracy rate of 95.65% in feature classification.

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Acknowledgements

This work is financially supported by the National Natural Science Foundation of China (Nos. 61373159, 61471247, 61501307), the program for Liaoning Excellent Talents in University (LJQ2015095, LR2015057), the Shenyang Key Laboratory Project and the Open foundation of Key lab of Information Networking and Confrontation of Shenyang Ligong University (No. 4771004kfs18). It is also sponsored by “Liaoning BaiQianWan Talents Program (2014921044)”, General Project of Liaoning Provincial Committee of Education (No. L2015459).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shenyang Ligong University, Shenyang, 110159, Liaoning, China

    Ye Xu & Ye Tian

  2. Liaoning Academy of Governance, Shenyang, 110161, Liaoning, China

    Yun Chi

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  1. Ye Xu
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  2. Yun Chi
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  3. Ye Tian
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Correspondence to Ye Xu.

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Xu, Y., Chi, Y. & Tian, Y. Deep Convolutional Neural Networks for Feature Extraction of Images Generated from Complex Networks Topologies. Wireless Pers Commun 103, 327–338 (2018). https://doi.org/10.1007/s11277-018-5445-7

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  • DOI: https://doi.org/10.1007/s11277-018-5445-7

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