eCite Digital Repository

Comprehensive influence propagation modelling for hybrid social network

Citation

Li, W and Bai, Q and Zhang, M, Comprehensive influence propagation modelling for hybrid social network, 29th Australasian Joint Conference on Artificial Intelligence (AI 2016): Advances in Artificial Intelligence. Lecture Notes in Computer Science, volume 9992, 5-8 December 2016, Hobart, Tasmania, pp. 597-608. ISBN 978-3-319-50126-0 (2016) [Refereed Conference Paper]


Preview
PDF
718Kb
  

Copyright Statement

Copyright 2016 Springer

DOI: doi:10.1007/978-3-319-50127-7_53

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.

Item Details

Item Type:Refereed Conference Paper
Keywords:hybrid social network, indirect influence, influence propagation, influence maximization, agent-based modelling, influence propagation modelling
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Intelligent robotics
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Application software packages
UTAS Author:Bai, Q (Dr Quan Bai)
ID Code:140683
Year Published:2016
Deposited By:Information and Communication Technology
Deposited On:2020-09-01
Last Modified:2020-11-09
Downloads:4 View Download Statistics

Repository Staff Only: item control page