13,526 research outputs found
Living on the Edge: The Role of Proactive Caching in 5G Wireless Networks
This article explores one of the key enablers of beyond G wireless
networks leveraging small cell network deployments, namely proactive caching.
Endowed with predictive capabilities and harnessing recent developments in
storage, context-awareness and social networks, peak traffic demands can be
substantially reduced by proactively serving predictable user demands, via
caching at base stations and users' devices. In order to show the effectiveness
of proactive caching, we examine two case studies which exploit the spatial and
social structure of the network, where proactive caching plays a crucial role.
Firstly, in order to alleviate backhaul congestion, we propose a mechanism
whereby files are proactively cached during off-peak demands based on file
popularity and correlations among users and files patterns. Secondly,
leveraging social networks and device-to-device (D2D) communications, we
propose a procedure that exploits the social structure of the network by
predicting the set of influential users to (proactively) cache strategic
contents and disseminate them to their social ties via D2D communications.
Exploiting this proactive caching paradigm, numerical results show that
important gains can be obtained for each case study, with backhaul savings and
a higher ratio of satisfied users of up to and , respectively.
Higher gains can be further obtained by increasing the storage capability at
the network edge.Comment: accepted for publication in IEEE Communications Magazin
Network-based ranking in social systems: three challenges
Ranking algorithms are pervasive in our increasingly digitized societies,
with important real-world applications including recommender systems, search
engines, and influencer marketing practices. From a network science
perspective, network-based ranking algorithms solve fundamental problems
related to the identification of vital nodes for the stability and dynamics of
a complex system. Despite the ubiquitous and successful applications of these
algorithms, we argue that our understanding of their performance and their
applications to real-world problems face three fundamental challenges: (i)
Rankings might be biased by various factors; (2) their effectiveness might be
limited to specific problems; and (3) agents' decisions driven by rankings
might result in potentially vicious feedback mechanisms and unhealthy systemic
consequences. Methods rooted in network science and agent-based modeling can
help us to understand and overcome these challenges.Comment: Perspective article. 9 pages, 3 figure
Dynamical Patterns of Cattle Trade Movements
Despite their importance for the spread of zoonotic diseases, our
understanding of the dynamical aspects characterizing the movements of farmed
animal populations remains limited as these systems are traditionally studied
as static objects and through simplified approximations. By leveraging on the
network science approach, here we are able for the first time to fully analyze
the longitudinal dataset of Italian cattle movements that reports the mobility
of individual animals among farms on a daily basis. The complexity and
inter-relations between topology, function and dynamical nature of the system
are characterized at different spatial and time resolutions, in order to
uncover patterns and vulnerabilities fundamental for the definition of targeted
prevention and control measures for zoonotic diseases. Results show how the
stationarity of statistical distributions coexists with a strong and
non-trivial evolutionary dynamics at the node and link levels, on all
timescales. Traditional static views of the displacement network hide important
patterns of structural changes affecting nodes' centrality and farms' spreading
potential, thus limiting the efficiency of interventions based on partial
longitudinal information. By fully taking into account the longitudinal
dimension, we propose a novel definition of dynamical motifs that is able to
uncover the presence of a temporal arrow describing the evolution of the system
and the causality patterns of its displacements, shedding light on mechanisms
that may play a crucial role in the definition of preventive actions
Dynamical Patterns of Cattle Trade Movements
Despite their importance for the spread of zoonotic diseases, our
understanding of the dynamical aspects characterizing the movements of farmed
animal populations remains limited as these systems are traditionally studied
as static objects and through simplified approximations. By leveraging on the
network science approach, here we are able for the first time to fully analyze
the longitudinal dataset of Italian cattle movements that reports the mobility
of individual animals among farms on a daily basis. The complexity and
inter-relations between topology, function and dynamical nature of the system
are characterized at different spatial and time resolutions, in order to
uncover patterns and vulnerabilities fundamental for the definition of targeted
prevention and control measures for zoonotic diseases. Results show how the
stationarity of statistical distributions coexists with a strong and
non-trivial evolutionary dynamics at the node and link levels, on all
timescales. Traditional static views of the displacement network hide important
patterns of structural changes affecting nodes' centrality and farms' spreading
potential, thus limiting the efficiency of interventions based on partial
longitudinal information. By fully taking into account the longitudinal
dimension, we propose a novel definition of dynamical motifs that is able to
uncover the presence of a temporal arrow describing the evolution of the system
and the causality patterns of its displacements, shedding light on mechanisms
that may play a crucial role in the definition of preventive actions
Topology comparison of Twitter diffusion networks effectively reveals misleading information
In recent years, malicious information had an explosive growth in social
media, with serious social and political backlashes. Recent important studies,
featuring large-scale analyses, have produced deeper knowledge about this
phenomenon, showing that misleading information spreads faster, deeper and more
broadly than factual information on social media, where echo chambers,
algorithmic and human biases play an important role in diffusion networks.
Following these directions, we explore the possibility of classifying news
articles circulating on social media based exclusively on a topological
analysis of their diffusion networks. To this aim we collected a large dataset
of diffusion networks on Twitter pertaining to news articles published on two
distinct classes of sources, namely outlets that convey mainstream, reliable
and objective information and those that fabricate and disseminate various
kinds of misleading articles, including false news intended to harm, satire
intended to make people laugh, click-bait news that may be entirely factual or
rumors that are unproven. We carried out an extensive comparison of these
networks using several alignment-free approaches including basic network
properties, centrality measures distributions, and network distances. We
accordingly evaluated to what extent these techniques allow to discriminate
between the networks associated to the aforementioned news domains. Our results
highlight that the communities of users spreading mainstream news, compared to
those sharing misleading news, tend to shape diffusion networks with subtle yet
systematic differences which might be effectively employed to identify
misleading and harmful information.Comment: A revised new version is available on Scientific Report
A Systematic Identification and Analysis of Scientists on Twitter
Metrics derived from Twitter and other social media---often referred to as
altmetrics---are increasingly used to estimate the broader social impacts of
scholarship. Such efforts, however, may produce highly misleading results, as
the entities that participate in conversations about science on these platforms
are largely unknown. For instance, if altmetric activities are generated mainly
by scientists, does it really capture broader social impacts of science? Here
we present a systematic approach to identifying and analyzing scientists on
Twitter. Our method can identify scientists across many disciplines, without
relying on external bibliographic data, and be easily adapted to identify other
stakeholder groups in science. We investigate the demographics, sharing
behaviors, and interconnectivity of the identified scientists. We find that
Twitter has been employed by scholars across the disciplinary spectrum, with an
over-representation of social and computer and information scientists;
under-representation of mathematical, physical, and life scientists; and a
better representation of women compared to scholarly publishing. Analysis of
the sharing of URLs reveals a distinct imprint of scholarly sites, yet only a
small fraction of shared URLs are science-related. We find an assortative
mixing with respect to disciplines in the networks between scientists,
suggesting the maintenance of disciplinary walls in social media. Our work
contributes to the literature both methodologically and conceptually---we
provide new methods for disambiguating and identifying particular actors on
social media and describing the behaviors of scientists, thus providing
foundational information for the construction and use of indicators on the
basis of social media metrics
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