3,710 research outputs found
Phase transition for the Maki-Thompson rumour model on a small-world network
We consider the Maki-Thompson model for the stochastic propagation of a
rumour within a population. We extend the original hypothesis of homogenously
mixed population by allowing for a small-world network embedding the model.
This structure is realized starting from a -regular ring and by inserting,
in the average, additional links in such a way that and are
tuneable parameter for the population architecture. We prove that this system
exhibits a transition between regimes of localization (where the final number
of stiflers is at most logarithmic in the population size) and propagation
(where the final number of stiflers grows algebraically with the population
size) at a finite value of the network parameter . A quantitative estimate
for the critical value of is obtained via extensive numerical simulations.Comment: 24 pages, 4 figure
How to Run a Campaign: Optimal Control of SIS and SIR Information Epidemics
Information spreading in a population can be modeled as an epidemic.
Campaigners (e.g. election campaign managers, companies marketing products or
movies) are interested in spreading a message by a given deadline, using
limited resources. In this paper, we formulate the above situation as an
optimal control problem and the solution (using Pontryagin's Maximum Principle)
prescribes an optimal resource allocation over the time of the campaign. We
consider two different scenarios --- in the first, the campaigner can adjust a
direct control (over time) which allows her to recruit individuals from the
population (at some cost) to act as spreaders for the
Susceptible-Infected-Susceptible (SIS) epidemic model. In the second case, we
allow the campaigner to adjust the effective spreading rate by incentivizing
the infected in the Susceptible-Infected-Recovered (SIR) model, in addition to
the direct recruitment. We consider time varying information spreading rate in
our formulation to model the changing interest level of individuals in the
campaign, as the deadline is reached. In both the cases, we show the existence
of a solution and its uniqueness for sufficiently small campaign deadlines. For
the fixed spreading rate, we show the effectiveness of the optimal control
strategy against the constant control strategy, a heuristic control strategy
and no control. We show the sensitivity of the optimal control to the spreading
rate profile when it is time varying.Comment: Proofs for Theorems 4.2 and 5.2 which do not appear in the published
journal version are included in this version. Published version can be
accessed here: http://dx.doi.org/10.1016/j.amc.2013.12.16
Gossip in a Smartphone Peer-to-Peer Network
In this paper, we study the fundamental problem of gossip in the mobile
telephone model: a recently introduced variation of the classical telephone
model modified to better describe the local peer-to-peer communication services
implemented in many popular smartphone operating systems. In more detail, the
mobile telephone model differs from the classical telephone model in three
ways: (1) each device can participate in at most one connection per round; (2)
the network topology can undergo a parameterized rate of change; and (3)
devices can advertise a parameterized number of bits about their state to their
neighbors in each round before connection attempts are initiated. We begin by
describing and analyzing new randomized gossip algorithms in this model under
the harsh assumption of a network topology that can change completely in every
round. We prove a significant time complexity gap between the case where nodes
can advertise bits to their neighbors in each round, and the case where
nodes can advertise bit. For the latter assumption, we present two
solutions: the first depends on a shared randomness source, while the second
eliminates this assumption using a pseudorandomness generator we prove to exist
with a novel generalization of a classical result from the study of two-party
communication complexity. We then turn our attention to the easier case where
the topology graph is stable, and describe and analyze a new gossip algorithm
that provides a substantial performance improvement for many parameters. We
conclude by studying a relaxed version of gossip in which it is only necessary
for nodes to each learn a specified fraction of the messages in the system.Comment: Extended Abstract to Appear in the Proceedings of the ACM Conference
on the Principles of Distributed Computing (PODC 2017
Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making
It is widely believed that one's peers influence product adoption behaviors.
This relationship has been linked to the number of signals a decision-maker
receives in a social network. But it is unclear if these same principles hold
when the pattern by which it receives these signals vary and when peer
influence is directed towards choices which are not optimal. To investigate
that, we manipulate social signal exposure in an online controlled experiment
using a game with human participants. Each participant in the game makes a
decision among choices with differing utilities. We observe the following: (1)
even in the presence of monetary risks and previously acquired knowledge of the
choices, decision-makers tend to deviate from the obvious optimal decision when
their peers make similar decision which we call the influence decision, (2)
when the quantity of social signals vary over time, the forwarding probability
of the influence decision and therefore being responsive to social influence
does not necessarily correlate proportionally to the absolute quantity of
signals. To better understand how these rules of peer influence could be used
in modeling applications of real world diffusion and in networked environments,
we use our behavioral findings to simulate spreading dynamics in real world
case studies. We specifically try to see how cumulative influence plays out in
the presence of user uncertainty and measure its outcome on rumor diffusion,
which we model as an example of sub-optimal choice diffusion. Together, our
simulation results indicate that sequential peer effects from the influence
decision overcomes individual uncertainty to guide faster rumor diffusion over
time. However, when the rate of diffusion is slow in the beginning, user
uncertainty can have a substantial role compared to peer influence in deciding
the adoption trajectory of a piece of questionable information
Online Misinformation: Challenges and Future Directions
Misinformation has become a common part of our digital media environments and it is compromising the ability of our societies to form informed opinions. It generates misperceptions, which have affected the decision making processes in many domains, including economy, health, environment, and elections, among others. Misinformation and its generation, propagation, impact, and management is being studied through a variety of lenses (computer science, social science, journalism, psychology, etc.) since it widely affects multiple aspects of society. In this paper we analyse the phenomenon of misinformation from a technological point of view.We study the current socio-technical advancements towards addressing the problem, identify some of the key limitations of current technologies, and propose some ideas to target such limitations. The goal of this position paper is to reflect on the current state of the art and to stimulate discussions on the future design and development of algorithms, methodologies, and applications
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