From the Social Networks Analysis (SNA) perspective, Viral Marketing has the aim to maximize the number of people that become aware of a given product/service by identifying a few number of individuals, considered more “influential” that can be promoted products or services. In this paper we propose a novel concept of influence graph that can be easily derived by querying social network modelled as a graph. Furthermore, spread diffusion model has been defined as a Combinatorial Multi-Armed Bandit (CMAB) problem for retrieving the most influential users, without any kind of preliminary knowledge.
Querying and learning OSN graphs for advanced viral marketing applications / Cuzzocrea, A.; Picariello, A.; Moscato, V.; Sperli, G.. - (2019), pp. 117-121. (Intervento presentato al convegno 3rd International Conference on Cloud and Big Data Computing, ICCBDC 2019 tenutosi a St Anne's College in University of Oxford, gbr nel 2019) [10.1145/3358505.3358525].
Querying and learning OSN graphs for advanced viral marketing applications
Cuzzocrea A.;Picariello A.;Moscato V.;Sperli G.
2019
Abstract
From the Social Networks Analysis (SNA) perspective, Viral Marketing has the aim to maximize the number of people that become aware of a given product/service by identifying a few number of individuals, considered more “influential” that can be promoted products or services. In this paper we propose a novel concept of influence graph that can be easily derived by querying social network modelled as a graph. Furthermore, spread diffusion model has been defined as a Combinatorial Multi-Armed Bandit (CMAB) problem for retrieving the most influential users, without any kind of preliminary knowledge.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
