759 research outputs found
Emergence of influential spreaders in modified rumor models
The burst in the use of online social networks over the last decade has
provided evidence that current rumor spreading models miss some fundamental
ingredients in order to reproduce how information is disseminated. In
particular, recent literature has revealed that these models fail to reproduce
the fact that some nodes in a network have an influential role when it comes to
spread a piece of information. In this work, we introduce two mechanisms with
the aim of filling the gap between theoretical and experimental results. The
first model introduces the assumption that spreaders are not always active
whereas the second model considers the possibility that an ignorant is not
interested in spreading the rumor. In both cases, results from numerical
simulations show a higher adhesion to real data than classical rumor spreading
models. Our results shed some light on the mechanisms underlying the spreading
of information and ideas in large social systems and pave the way for more
realistic diffusion models.Comment: 14 Pages, 6 figures, accepted for publication in Journal of
Statistical Physic
Detecting the Influence of Spreading in Social Networks with Excitable Sensor Networks
Detecting spreading outbreaks in social networks with sensors is of great
significance in applications. Inspired by the formation mechanism of human's
physical sensations to external stimuli, we propose a new method to detect the
influence of spreading by constructing excitable sensor networks. Exploiting
the amplifying effect of excitable sensor networks, our method can better
detect small-scale spreading processes. At the same time, it can also
distinguish large-scale diffusion instances due to the self-inhibition effect
of excitable elements. Through simulations of diverse spreading dynamics on
typical real-world social networks (facebook, coauthor and email social
networks), we find that the excitable senor networks are capable of detecting
and ranking spreading processes in a much wider range of influence than other
commonly used sensor placement methods, such as random, targeted, acquaintance
and distance strategies. In addition, we validate the efficacy of our method
with diffusion data from a real-world online social system, Twitter. We find
that our method can detect more spreading topics in practice. Our approach
provides a new direction in spreading detection and should be useful for
designing effective detection methods
Sequential Attention Source Identification Based on Feature Representation
Snapshot observation based source localization has been widely studied due to
its accessibility and low cost. However, the interaction of users in existing
methods does not be addressed in time-varying infection scenarios. So these
methods have a decreased accuracy in heterogeneous interaction scenarios. To
solve this critical issue, this paper proposes a sequence-to-sequence based
localization framework called Temporal-sequence based Graph Attention Source
Identification (TGASI) based on an inductive learning idea. More specifically,
the encoder focuses on generating multiple features by estimating the influence
probability between two users, and the decoder distinguishes the importance of
prediction sources in different timestamps by a designed temporal attention
mechanism. It's worth mentioning that the inductive learning idea ensures that
TGASI can detect the sources in new scenarios without knowing other prior
knowledge, which proves the scalability of TGASI. Comprehensive experiments
with the SOTA methods demonstrate the higher detection performance and
scalability in different scenarios of TGASI
Observer Placement for Source Localization: The Effect of Budgets and Transmission Variance
When an epidemic spreads in a network, a key question is where was its
source, i.e., the node that started the epidemic. If we know the time at which
various nodes were infected, we can attempt to use this information in order to
identify the source. However, maintaining observer nodes that can provide their
infection time may be costly, and we may have a budget on the number of
observer nodes we can maintain. Moreover, some nodes are more informative than
others due to their location in the network. Hence, a pertinent question
arises: Which nodes should we select as observers in order to maximize the
probability that we can accurately identify the source? Inspired by the simple
setting in which the node-to-node delays in the transmission of the epidemic
are deterministic, we develop a principled approach for addressing the problem
even when transmission delays are random. We show that the optimal
observer-placement differs depending on the variance of the transmission delays
and propose approaches in both low- and high-variance settings. We validate our
methods by comparing them against state-of-the-art observer-placements and show
that, in both settings, our approach identifies the source with higher
accuracy.Comment: Accepted for presentation at the 54th Annual Allerton Conference on
Communication, Control, and Computin
The Anatomy of a Scientific Rumor
The announcement of the discovery of a Higgs boson-like particle at CERN will
be remembered as one of the milestones of the scientific endeavor of the 21st
century. In this paper we present a study of information spreading processes on
Twitter before, during and after the announcement of the discovery of a new
particle with the features of the elusive Higgs boson on 4th July 2012. We
report evidence for non-trivial spatio-temporal patterns in user activities at
individual and global level, such as tweeting, re-tweeting and replying to
existing tweets. We provide a possible explanation for the observed
time-varying dynamics of user activities during the spreading of this
scientific "rumor". We model the information spreading in the corresponding
network of individuals who posted a tweet related to the Higgs boson discovery.
Finally, we show that we are able to reproduce the global behavior of about
500,000 individuals with remarkable accuracy.Comment: 11 pages, 8 figure
Reconstructing propagation networks with natural diversity and identifying hidden sources
Our ability to uncover complex network structure and dynamics from data is
fundamental to understanding and controlling collective dynamics in complex
systems. Despite recent progress in this area, reconstructing networks with
stochastic dynamical processes from limited time series remains to be an
outstanding problem. Here we develop a framework based on compressed sensing to
reconstruct complex networks on which stochastic spreading dynamics take place.
We apply the methodology to a large number of model and real networks, finding
that a full reconstruction of inhomogeneous interactions can be achieved from
small amounts of polarized (binary) data, a virtue of compressed sensing.
Further, we demonstrate that a hidden source that triggers the spreading
process but is externally inaccessible can be ascertained and located with high
confidence in the absence of direct routes of propagation from it. Our approach
thus establishes a paradigm for tracing and controlling epidemic invasion and
information diffusion in complex networked systems.Comment: 20 pages and 5 figures. For Supplementary information, please see
http://www.nature.com/ncomms/2014/140711/ncomms5323/full/ncomms5323.html#
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