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140683 - Comprehensive influence propagation modelling for hybrid social network.pdf (718.18 kB)

Comprehensive influence propagation modelling for hybrid social network

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conference contribution
posted on 2023-05-23, 14:41 authored by Li, W, Quan BaiQuan Bai, Zhang, M
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.

History

Publication title

29th Australasian Joint Conference on Artificial Intelligence (AI 2016): Advances in Artificial Intelligence. Lecture Notes in Computer Science, volume 9992

Editors

B Kang and Q Bai

Pagination

597-608

ISBN

978-3-319-50126-0

Department/School

School of Information and Communication Technology

Publisher

Springer

Place of publication

New York, United States

Event title

29th Australasian Joint Conference on Artificial Intelligence (AI 2016): Advances in Artificial Intelligence

Event Venue

Hobart, Tasmania

Date of Event (Start Date)

2016-12-05

Date of Event (End Date)

2016-12-08

Rights statement

Copyright 2016 Springer

Repository Status

  • Open

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