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Big Data Optimization: Recent Developments and Challenges pp 345–373Cite as

Big Network Analytics Based on Nonconvex Optimization

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Part of the book series: Studies in Big Data ((SBD,volume 18))

Abstract

The scientific problems that Big Data faces may be network scientific problems. Network analytics contributes a great deal to networked Big Data processing. Many network issues can be modeled as nonconvex optimization problems and consequently they can be addressed by optimization techniques. In the pipeline of nonconvex optimization techniques, evolutionary computation gives an outlet to handle these problems efficiently. Because, network community discovery is a critical research agenda of network analytics, in this chapter we focus on the evolutionary computation based nonconvex optimization for network community discovery. The single and multiple objective optimization models for the community discovery problem are thoroughly investigated. Several experimental studies are shown to demonstrate the effectiveness of optimization based approach for big network community analytics.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant nos. 61273317, 61422209, and 61473215), the National Top Youth Talents Program of China, and the Specialized Research Fund for the Doctoral Program of Higher Education (Grant no. 20130203110011).

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  1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Shaanxi Province, Xi’an, 710071, China

    Maoguo Gong, Qing Cai, Lijia Ma & Licheng Jiao

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  1. Maoguo Gong
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  2. Qing Cai
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  3. Lijia Ma
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  4. Licheng Jiao
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Correspondence to Maoguo Gong .

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  1. Aston Business School, Aston University, Birmingham, United Kingdom

    Ali Emrouznejad

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Gong, M., Cai, Q., Ma, L., Jiao, L. (2016). Big Network Analytics Based on Nonconvex Optimization. In: Emrouznejad, A. (eds) Big Data Optimization: Recent Developments and Challenges. Studies in Big Data, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-319-30265-2_15

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  • DOI: https://doi.org/10.1007/978-3-319-30265-2_15

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