Emering problems in influence propagation and maximization
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Caliò, Antonio
Crupi, Felice
Tagarelli, Andrea
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Dottorato di ricerca in Information and Communication Technologies. Ciclo XXXIII; In the last two decades we witnessed the advent and the rapid growth of online social
networks (OSNs). The impact of their pervasive diffusion on everyday life has been
dramatic. In fact, social networks changed the way we interact with each other,
the way we access information and the way companies engage with their audience
or customers. A major consequence of the broad adoption and diffusion of social
networks is the availability of an unprecedented amount of user data, which enables
the opportunity for social and network scientists to investigate and observe many
facets of human behaviors. Arguably, one of the most interesting facet is related to
the notion of social influence.
Following this observation, this research project is mainly centered around the
concept of social influence, specifically its propagation and maximization. Therefore,
the goal of this thesis is twofold. To begin with, we investigate the complexity of influence
propagation in real-world contexts. This leads to the definition of a novel class
of diffusion models. Such models represent an attempt to unify, under a well-defined
framework, all the aspects that contribute to the inherent complexity of any influence
propagation phenomena. Afterwards, we devote our attention to the influence
maximization problem. To this purpose, we first provide a detailed characterization
of social influence from a topological perspective. Specifically, we want to understand
if and to what extent being a good spreader depends on being located into strategic
regions of a network.
Finally, we focus on the application of the influence maximization problem. In
particular, we address a variant of the original problem, which is especially suitable
for viral marketing scenarios. To this end, we propose two different diversity-sensitive
targeted influence maximization problems. Both proposals share a common intent,
which is assessing the benefit of embedding a notion of diversity into the process
of the seeds identification. Nonetheless, diversity is considered from two different
perspectives: (i) as a function of the topological properties of the nodes; (ii) as a
function of some categorical data available on the node level.Soggetto
Network Science; Data Mining; Machine Learning optimization; Viral marketing; Research Subject Categories::TECHNOLOGY::Information technology::Computer science
Relazione
ING-INF/05;