2 research outputs found
Influence maximization based on the least influential spreaders
The emergence of social media increases the need for the recognization of social influence mainly motivated by online advertising, political and health campaigns, recommendation systems, epidemiological study, etc. In spreading processes, it is possible to define the most central or influential vertices according to the network topology and dynamic. On the other hand, the least influential spreaders have been disregarded. This paper aims to maximize the mean of information propagation on the network by recognizing the non influential individuals by making them better spreader. Experimental results confirm that selecting 0.5% of least influential spreaders in three social networks (google+, hamsterster and advogato) and rewiring one connection to some important vertex, increase the propagation over the entire network.National Council for Scientific and Technological Development (CNPq) (grant: 140688/2013-7)Sao Paulo Research Foundation (FAPESP) (grant: 2011/21880-3
The Impact of Social Curiosity on Information Spreading on Networks
Most information spreading models consider that all individuals are identical
psychologically. They ignore, for instance, the curiosity level of people,
which may indicate that they can be influenced to seek for information given
their interest. For example, the game Pok\'emon GO spread rapidly because of
the aroused curiosity among users. This paper proposes an information
propagation model considering the curiosity level of each individual, which is
a dynamical parameter that evolves over time. We evaluate the efficiency of our
model in contrast to traditional information propagation models, like SIR or
IC, and perform analysis on different types of artificial and real-world
networks, like Google+, Facebook, and the United States roads map. We present a
mean-field approach that reproduces with a good accuracy the evolution of
macroscopic quantities, such as the density of stiflers, for the system's
behavior with the curiosity. We also obtain an analytical solution of the
mean-field equations that allows to predicts a transition from a phase where
the information remains confined to a small number of users to a phase where it
spreads over a large fraction of the population. The results indicate that the
curiosity increases the information spreading in all networks as compared with
the spreading without curiosity, and that this increase is larger in spatial
networks than in social networks. When the curiosity is taken into account, the
maximum number of informed individuals is reached close to the transition
point. Since curious people are more open to a new product, concepts, and
ideas, this is an important factor to be considered in propagation modeling.
Our results contribute to the understanding of the interplay between diffusion
process and dynamical heterogeneous transmission in social networks.Comment: 8 pages, 5 figure