247,865 research outputs found
Interests Diffusion in Social Networks
Understanding cultural phenomena on Social Networks (SNs) and exploiting the
implicit knowledge about their members is attracting the interest of different
research communities both from the academic and the business side. The
community of complexity science is devoting significant efforts to define laws,
models, and theories, which, based on acquired knowledge, are able to predict
future observations (e.g. success of a product). In the mean time, the semantic
web community aims at engineering a new generation of advanced services by
defining constructs, models and methods, adding a semantic layer to SNs. In
this context, a leapfrog is expected to come from a hybrid approach merging the
disciplines above. Along this line, this work focuses on the propagation of
individual interests in social networks. The proposed framework consists of the
following main components: a method to gather information about the members of
the social networks; methods to perform some semantic analysis of the Domain of
Interest; a procedure to infer members' interests; and an interests evolution
theory to predict how the interests propagate in the network. As a result, one
achieves an analytic tool to measure individual features, such as members'
susceptibilities and authorities. Although the approach applies to any type of
social network, here it is has been tested against the computer science
research community.
The DBLP (Digital Bibliography and Library Project) database has been elected
as test-case since it provides the most comprehensive list of scientific
production in this field.Comment: 30 pages 13 figs 4 table
Weak ties: Subtle role of information diffusion in online social networks
As a social media, online social networks play a vital role in the social
information diffusion. However, due to its unique complexity, the mechanism of
the diffusion in online social networks is different from the ones in other
types of networks and remains unclear to us. Meanwhile, few works have been
done to reveal the coupled dynamics of both the structure and the diffusion of
online social networks. To this end, in this paper, we propose a model to
investigate how the structure is coupled with the diffusion in online social
networks from the view of weak ties. Through numerical experiments on
large-scale online social networks, we find that in contrast to some previous
research results, selecting weak ties preferentially to republish cannot make
the information diffuse quickly, while random selection can achieve this goal.
However, when we remove the weak ties gradually, the coverage of the
information will drop sharply even in the case of random selection. We also
give a reasonable explanation for this by extra analysis and experiments.
Finally, we conclude that weak ties play a subtle role in the information
diffusion in online social networks. On one hand, they act as bridges to
connect isolated local communities together and break through the local
trapping of the information. On the other hand, selecting them as preferential
paths to republish cannot help the information spread further in the network.
As a result, weak ties might be of use in the control of the virus spread and
the private information diffusion in real-world applications.Comment: Final version published in PR
Marketing Impact on Diffusion in Social Networks
The paper proposes a way to add marketing into the standard threshold model
of social networks. Within this framework, the paper studies logical properties
of the influence relation between sets of agents in social networks. Two
different forms of this relation are considered: one for promotional marketing
and the other for preventive marketing. In each case a sound and complete
logical system describing properties of the influence relation is proposed.
Both systems could be viewed as extensions of Armstrong's axioms of functional
dependency from the database theory
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