An enriched social behavioural information diffusion model in social networks

Abstract

Online social networks have recently become an innovative and effective method for spreading information among people around the world. Information diffusion, rumour spreading and diseases infection are all instances of stochastic processes that occur over the edges of social networks. Many prior works have carried out empirical studies and diffusion models to understand how information propagates in online social networks; however they suffer from problems. In this paper, we propose an information diffusion model inspired by information propagation among people. Our proposed Social Behavioural Information Diffusion Model, abbreviated as SBIDM, considers the effect of mainstream media like TV and radio, as well as interaction with the neighbours. The advantages of our approach are four-fold. First, it models information diffusion in social networks inspired by social life, which considers the effect of aggregate social behaviour to diffuse information; second, it allows partial knowledge to be held in each individual; third, it considers the effects of social media in propagating information as well as the effects of interacting with neighbours; and last but not least, it is applicable to different types of data including synthetic and well-known real social networks like Facebook, Amazon, Epinions and DBLP. To explore the advantages of our approach, many experiments with different settings and specifications were conducted. The obtained results are very promising.

La Influencia de las Redes Sociales Virtuales en la Difusión de Información y Conocimiento: Estudio de PyMES

“…Dimensión OSN e ID Número Referencias de artículos analizados Sugerencia de conductas 3 (Agrawal et al, 2011), (Aragon et al, 2013, (Chelmis et al, 2013), (Chelmis et al 2014), (Crowne et al, 2015), (Garg et al, 2011), (Heimbach & Hinz, 2016), (Jiang et al, 2014), (Korzynski, 2014), (Levy et al, 2016), (Li et al, 2016), (Liang et al, 2016), (Lim et al, 2011), (Lin et al, 2012), (Lin et al, 2014), (Lipizzi et al, 2016), (Mozafari & Hamzeh, 2015), (Palacios -Marquez et al, 2016), (Roy & Zeng, 2015), (Sankar & Ravindran, 2015), (Smith, 2013), (Subires-Mancera & Olmedo Salar, 2013), (Tang et al, 2015), (Wadhwa & Bhatia, 2015), , (Wu et al, 2015), (Zaglia et al, 2015), (Zhang, 2015), (Zhao et al, 2011), (Zinoviev, 2011).…”

Modelling information diffusion based on non-dominated friends in social networks

“…A social behavioural information diffusion model (SBIDM) has been proposed in Mozafari and Hamzeh [12] to extend particle swarm optimisation diffusion model (PSODM) [29]. SBIDM considered the effect of mainstream media like TV, radio and so on as well as interaction with the neighbours in propagation of information.…”

“…IDNDF takes into account that information perception component is based on one of non-dominated friends and this is obtained using different factors simultaneously. One of the factors that affect the decision of people for selecting a neighbour as source of information is the amount of information possessed by each individual during communication which has been called score in previous models [12,29]. The amount of information or score for each individual is calculated based on sum of elements of the state vector.…”

“…There exist several works that have empirically studied information diffusion modelling to measure the popularity of news [19–27]. A few recent methods propose mathematical approaches to model information diffusion in social networks [9,12,28–31].…”

“…Previous information diffusion models assumed that information perception is just based on a single factor [12,29,30,36], whereas in reality, there exist different factors for selecting a neighbour who would supply information. For example, you would like to select one of your friends to receive information about programming language, and supposing that one of your friends has a PhD degree in that field and he or she could be a good choice since he or she has a vast amount of information (score).…”