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A Semi-supervised Multi-objective Evolutionary Algorithm for Multi-layer Network Community Detection

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Knowledge Science, Engineering and Management (KSEM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12815))

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Abstract

In the real world, many complex systems can be abstracted as multi-layer networks. Recently, community detection for multi-layer networks plays a vital role in multi-relationship complex system analysis, thus gradually gaining popularity especially in the optimization algorithms. The multi-objective optimization (MOOP) methods attract attention owing to the flexibility in solving community detection problems. Nevertheless, most of the MOOP methods pay little attention to the prior information, which cannot ensure the high-level accuracy and robustness against networks with complicated community structures. To address the problem, this paper proposes a semi-supervised multi-objective evolutionary algorithm for multi-layer community detection (SS-MOML). The SS-MOML mainly consists of two steps: First, it extracts the prior information from the network. Second, based on the prior information, the prior layer is constructed by creating virtual connections and the high-quality initial population is generated. And then the optimization process begins, in which the genetic operation based on the prior information is committed to guiding the evolutionary direction of chromosomes. Some extensive experiments are implemented and the results prove that the proposed SS-MOML stands out in accuracy and robustness than 7 state-of-the-art multi-layer community detection algorithms.

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Acknowledgement

This work was supported by the National Key R&D Program of China (No. 2020AAA0107700), National Natural Science Foundation of China (Nos. 61976181, 11931015), Key Technology Research and Development Program of Science and Technology Scientific and Technological Innovation Team of Shaanxi Province (No. 2020TD-013) and the Science and Technology Support Program of Guizhou (No. QKHZC2021YB531) and National Municipal Training Program of Innovation and Entrepreneurship for Undergraduates (No. S202110635042).

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Correspondence to Chao Gao .

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Yin, Z., Deng, Y., Zhang, F., Luo, Z., Zhu, P., Gao, C. (2021). A Semi-supervised Multi-objective Evolutionary Algorithm for Multi-layer Network Community Detection. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12815. Springer, Cham. https://doi.org/10.1007/978-3-030-82136-4_15

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  • DOI: https://doi.org/10.1007/978-3-030-82136-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82135-7

  • Online ISBN: 978-3-030-82136-4

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