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Comprehensive Influence Propagation Modelling for Hybrid Social Network

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AI 2016: Advances in Artificial Intelligence (AI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9992))

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Abstract

The evolution of influencer marketing relies on a social phenomenon, i.e., influence diffusion. The modelling and analysis of influence propagation in social networks has been extensively investigated by both researchers and practitioners. Nearly all of the works in this field assume influence is driven by a single factor, e.g., friendship affiliation. However, influence spread through many other pathways, such as face-to-face interactions, phone calls, emails, or even through the reviews posted on web-pages. In this paper, we modelled the influence-diffusion space as a hybrid social network, where both direct and indirect influence are considered. Furthermore, a concrete implementation of hybrid social network, i.e., Comprehensive Influence Propagation model is articulated. The proposed model can be applied as an effective approach to tackle the multi-faceted influence diffusion problems in social networks. We also evaluated the proposed model in the influence maximization problem in different scenarios. Experimental results reveal that the proposed model can perform better than those considering a single aspect of influence.

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Notes

  1. 1.

    https://grouplens.org/datasets/movielens.

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Authors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Weihua Li & Quan Bai

  2. University of Wollongong, Wollongong, Australia

    Minjie Zhang

Authors
  1. Weihua Li
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  2. Quan Bai
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  3. Minjie Zhang
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Corresponding author

Correspondence to Weihua Li .

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Editors and Affiliations

  1. University of Tasmania, Hobart, Australia

    Byeong Ho Kang

  2. Auckland University of Technology, Auckland, New Zealand

    Quan Bai

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Li, W., Bai, Q., Zhang, M. (2016). Comprehensive Influence Propagation Modelling for Hybrid Social Network. In: Kang, B.H., Bai, Q. (eds) AI 2016: Advances in Artificial Intelligence. AI 2016. Lecture Notes in Computer Science(), vol 9992. Springer, Cham. https://doi.org/10.1007/978-3-319-50127-7_53

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  • DOI: https://doi.org/10.1007/978-3-319-50127-7_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50126-0

  • Online ISBN: 978-3-319-50127-7

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