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A Holistic Approach to Influence Maximization

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Hybrid Intelligence for Social Networks

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Abstract

A social network is an Internet-based collaboration platform that plays a vital role in information spread, opinion-forming, trend-setting, and keeps everyone connected. Moreover, the popularity of web and social networks has interesting applications including viral marketing, recommendation systems, poll analysis, etc. In these applications, user influence plays an important role. This chapter discusses how effectively social networks can be used for information propagation in the context of viral marketing. Picking the right group of users, hoping they will cause a chain effect of marketing, is the core of viral marketing applications. The strategy used to select the correct group of users is the influence maximization problem.

This chapter proposes one of the viable solutions to influence maximization. The focus is to find those users in the social networks who would adopt and propagate information, thus resulting in an effective marketing strategy. The three main components that would help in the effective spread of information in the social networks are: the network structure, the user’s influence on others, and the seeding algorithm. Amalgamation of these three aspects provides a holistic solution to influence maximization.

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Notes

  1. 1.

    https://www.microsoft.com/en-us/research/people/weic/#publications

  2. 2.

    http://socialcomputing.asu.edu

  3. 3.

    http://tuvalu.santafe.edu/~aaronc/powerlaws

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Authors and Affiliations

  1. National Institute of Technology Karnataka, Surathkal, Mangaluru, India

    Nireshwalya Sumith & Basava Annappa

  2. Jadavpur University, Kolkata, India

    Swapan Bhattacharya

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  1. Nireshwalya Sumith
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  2. Basava Annappa
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  3. Swapan Bhattacharya
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Correspondence to Nireshwalya Sumith .

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Editors and Affiliations

  1. Dept. of Computer Science, Dyal Singh College, University of Delhi, Delhi, India

    Hema Banati

  2. Dept. of Information Technology, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Siddhartha Bhattacharyya

  3. Dept. of Electrical & Electronics Engineering, Amity University, Noida, Uttar Pradesh, India

    Ashish Mani

  4. Network Design and Research Center, Kyushu Institute of Technology, Fukuoka, Japan

    Mario Köppen

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Sumith, N., Annappa, B., Bhattacharya, S. (2017). A Holistic Approach to Influence Maximization. In: Banati, H., Bhattacharyya, S., Mani, A., Köppen, M. (eds) Hybrid Intelligence for Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-65139-2_6

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