89,795 research outputs found
Self-Organizing Flows in Social Networks
Social networks offer users new means of accessing information, essentially
relying on "social filtering", i.e. propagation and filtering of information by
social contacts. The sheer amount of data flowing in these networks, combined
with the limited budget of attention of each user, makes it difficult to ensure
that social filtering brings relevant content to the interested users. Our
motivation in this paper is to measure to what extent self-organization of the
social network results in efficient social filtering. To this end we introduce
flow games, a simple abstraction that models network formation under selfish
user dynamics, featuring user-specific interests and budget of attention. In
the context of homogeneous user interests, we show that selfish dynamics
converge to a stable network structure (namely a pure Nash equilibrium) with
close-to-optimal information dissemination. We show in contrast, for the more
realistic case of heterogeneous interests, that convergence, if it occurs, may
lead to information dissemination that can be arbitrarily inefficient, as
captured by an unbounded "price of anarchy". Nevertheless the situation differs
when users' interests exhibit a particular structure, captured by a metric
space with low doubling dimension. In that case, natural autonomous dynamics
converge to a stable configuration. Moreover, users obtain all the information
of interest to them in the corresponding dissemination, provided their budget
of attention is logarithmic in the size of their interest set
A process of rumor scotching on finite populations
Rumor spreading is a ubiquitous phenomenon in social and technological
networks. Traditional models consider that the rumor is propagated by pairwise
interactions between spreaders and ignorants. Spreaders can become stiflers
only after contacting spreaders or stiflers. Here we propose a model that
considers the traditional assumptions, but stiflers are active and try to
scotch the rumor to the spreaders. An analytical treatment based on the theory
of convergence of density dependent Markov chains is developed to analyze how
the final proportion of ignorants behaves asymptotically in a finite
homogeneously mixing population. We perform Monte Carlo simulations in random
graphs and scale-free networks and verify that the results obtained for
homogeneously mixing populations can be approximated for random graphs, but are
not suitable for scale-free networks. Furthermore, regarding the process on a
heterogeneous mixing population, we obtain a set of differential equations that
describes the time evolution of the probability that an individual is in each
state. Our model can be applied to study systems in which informed agents try
to stop the rumor propagation. In addition, our results can be considered to
develop optimal information dissemination strategies and approaches to control
rumor propagation.Comment: 13 pages, 11 figure
Mobile Social Networking aided content dissemination in heterogeneous networks
Since more and more mobile applications are based on the proliferation of social information, the study of Mobile Social Net-works (MSNs) combines social sciences and wireless communications. Operating wireless networks more efficiently by exploiting social relationships between MSN users is an appealing but challenging option for network operators. An MSN-aided content dissemination technique is presented as a potential ex-tension of conventional cellular wireless net-works in order to satisfy growing data traffic. By allowing the MSN users to create a self-organized ad hoc network for spontaneously disseminating contents, the network operator may be able to reduce the operational costs and simultaneously achieve an improved network performance. In this paper, we first summarize the basic features of the MSN architecture, followed by a survey of the factors which may affect MSN-aided content dissemination. Using a case study, we demonstrate that one can save resources of the Base Station (BS) while substantially lowering content dissemination delay. Finally, other potential applications of MSN-aided content dissemination are introduced, and a range of future challenges are summarized
Joint Head Selection and Airtime Allocation for Data Dissemination in Mobile Social Networks
Mobile social networks (MSNs) enable people with similar interests to
interact without Internet access. By forming a temporary group, users can
disseminate their data to other interested users in proximity with short-range
communication technologies. However, due to user mobility, airtime available
for users in the same group to disseminate data is limited. In addition, for
practical consideration, a star network topology among users in the group is
expected. For the former, unfair airtime allocation among the users will
undermine their willingness to participate in MSNs. For the latter, a group
head is required to connect other users. These two problems have to be properly
addressed to enable real implementation and adoption of MSNs. To this aim, we
propose a Nash bargaining-based joint head selection and airtime allocation
scheme for data dissemination within the group. Specifically, the bargaining
game of joint head selection and airtime allocation is first formulated. Then,
Nash bargaining solution (NBS) based optimization problems are proposed for a
homogeneous case and a more general heterogeneous case. For both cases, the
existence of solution to the optimization problem is proved, which guarantees
Pareto optimality and proportional fairness. Next, an algorithm, allowing
distributed implementation, for join head selection and airtime allocation is
introduced. Finally, numerical results are presented to evaluate the
performance, validate intuitions and derive insights of the proposed scheme
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