A Novel Influence Diffusion Model based on User Generated Content in Online Social Networks

Flora Amato, Antonio Bosco, Vincenzo Moscato, Antonio Picariello, Giancarlo Sperlí

2017

Abstract

Social Network Analysis has been introduced to study the properties of Online Social Networks for a wide range of real life applications. In this paper, we propose a novel methodology for solving the Influence Maximization problem, i.e. the problem of finding a small subset of actors in a social network that could maximize the spread of influence. In particular, we define a novel influence diffusion model that, learning recurrent user behaviours from past logs, estimates the probability that a given user can influence the other ones, basically exploiting user to content actions. A greedy maximization algorithm is then adopted to determine the final set of influentials in the network. Preliminary experimental results shows the goodness of the proposed approach, especially in terms of efficiency, and encourage future research in such direction.

Paper Citation

in Harvard Style

Amato F., Bosco A., Moscato V., Picariello A. and Sperlí G. (2017). A Novel Influence Diffusion Model based on User Generated Content in Online Social Networks . In - KomIS, ISBN , pages 0-0. DOI: 10.5220/0006486703140320

in Bibtex Style

@conference{komis17,
author={Flora Amato and Antonio Bosco and Vincenzo Moscato and Antonio Picariello and Giancarlo Sperlí},
title={A Novel Influence Diffusion Model based on User Generated Content in Online Social Networks},
booktitle={ - KomIS,},
year={2017},
pages={},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006486703140320},
isbn={},
}

in EndNote Style

TY - CONF
JO - - KomIS,
TI - A Novel Influence Diffusion Model based on User Generated Content in Online Social Networks
SN -
AU - Amato F.
AU - Bosco A.
AU - Moscato V.
AU - Picariello A.
AU - Sperlí G.
PY - 2017
SP - 0
EP - 0
DO - 10.5220/0006486703140320