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Machine Learning for Auspicious Social Network Mining

  • Chapter
Social Networking

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 65))

Abstract

The importance of machine learning for social network analysis is realized as an inevitable tool in forthcoming years. This is due to the unprecedented growth of social-related data, boosted by the proliferation of social media websites and the embedded heterogeneity and complexity. Alongside the machine learning derives much effort from psychologists to build computational model for solving tasks like recognition, prediction, planning and analysis even in uncertain situations. In this chapter, we have presented different network analysis concepts. Then we have discussed implication of machine learning for network data preparation and different learning techniques for descriptive and predictive analysis. Finally we have presented some machine learning based findings in the area of community detection, prediction, spatial-temporal and fuzzy analysis.

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Authors and Affiliations

  1. S. N. Bose National Centre for Basic Sciences, Block-JD, Sector-III, Salt Lake, Kolkata, 98, India

    Sagar S. De

  2. Department of Systems Engineering, Ajou University, San 5, Woncheon-dong, Yeongtong-gu, Suwon, 443-749, Republic of Korea

    Satchidananda Dehuri

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  1. Sagar S. De
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  2. Satchidananda Dehuri
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Correspondence to Sagar S. De .

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Editors and Affiliations

  1. Gandhi Institute for Technological Advancement, Bhubaneswar, Orissa, India

    Mrutyunjaya Panda

  2. Department of Systems Engineering, Ajou University, Yeongtong-gu, Korea, Republic of (South Korea)

    Satchidananda Dehuri

  3. Department of Industrial Engineering, Ajou University, Yeongtong-gu, Korea, Republic of (South Korea)

    Gi-Nam Wang

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De, S.S., Dehuri, S. (2014). Machine Learning for Auspicious Social Network Mining. In: Panda, M., Dehuri, S., Wang, GN. (eds) Social Networking. Intelligent Systems Reference Library, vol 65. Springer, Cham. https://doi.org/10.1007/978-3-319-05164-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-05164-2_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05163-5

  • Online ISBN: 978-3-319-05164-2

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