189,376 research outputs found
Early Warning Analysis for Social Diffusion Events
There is considerable interest in developing predictive capabilities for
social diffusion processes, for instance to permit early identification of
emerging contentious situations, rapid detection of disease outbreaks, or
accurate forecasting of the ultimate reach of potentially viral ideas or
behaviors. This paper proposes a new approach to this predictive analytics
problem, in which analysis of meso-scale network dynamics is leveraged to
generate useful predictions for complex social phenomena. We begin by deriving
a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes
taking place over social networks with realistic topologies; this modeling
approach is inspired by recent work in biology demonstrating that S-HDS offer a
useful mathematical formalism with which to represent complex, multi-scale
biological network dynamics. We then perform formal stochastic reachability
analysis with this S-HDS model and conclude that the outcomes of social
diffusion processes may depend crucially upon the way the early dynamics of the
process interacts with the underlying network's community structure and
core-periphery structure. This theoretical finding provides the foundations for
developing a machine learning algorithm that enables accurate early warning
analysis for social diffusion events. The utility of the warning algorithm, and
the power of network-based predictive metrics, are demonstrated through an
empirical investigation of the propagation of political memes over social media
networks. Additionally, we illustrate the potential of the approach for
security informatics applications through case studies involving early warning
analysis of large-scale protests events and politically-motivated cyber
attacks
STAND: A Spatio-Temporal Algorithm for Network Diffusion Simulation
Information, ideas, and diseases, or more generally, contagions, spread over
space and time through individual transmissions via social networks, as well as
through external sources. A detailed picture of any diffusion process can be
achieved only when both a good network structure and individual diffusion
pathways are obtained. The advent of rich social, media and locational data
allows us to study and model this diffusion process in more detail than
previously possible. Nevertheless, how information, ideas or diseases are
propagated through the network as an overall process is difficult to trace.
This propagation is continuous over space and time, where individual
transmissions occur at different rates via complex, latent connections.
To tackle this challenge, a probabilistic spatiotemporal algorithm for
network diffusion (STAND) is developed based on the survival model in this
research. Both time and spatial distance are used as explanatory variables to
simulate the diffusion process over two different network structures. The aim
is to provide a more detailed measure of how different contagions are
transmitted through various networks where nodes are geographic places at a
large scale
Mean-field diffusive dynamics on weighted networks
Diffusion is a key element of a large set of phenomena occurring on natural
and social systems modeled in terms of complex weighted networks. Here, we
introduce a general formalism that allows to easily write down mean-field
equations for any diffusive dynamics on weighted networks. We also propose the
concept of annealed weighted networks, in which such equations become exact. We
show the validity of our approach addressing the problem of the random walk
process, pointing out a strong departure of the behavior observed in quenched
real scale-free networks from the mean-field predictions. Additionally, we show
how to employ our formalism for more complex dynamics. Our work sheds light on
mean-field theory on weighted networks and on its range of validity, and warns
about the reliability of mean-field results for complex dynamics.Comment: 8 pages, 3 figure
Entropy Rate of Diffusion Processes on Complex Networks
The concept of entropy rate for a dynamical process on a graph is introduced.
We study diffusion processes where the node degrees are used as a local
information by the random walkers. We describe analitically and numerically how
the degree heterogeneity and correlations affect the diffusion entropy rate. In
addition, the entropy rate is used to characterize complex networks from the
real world. Our results point out how to design optimal diffusion processes
that maximize the entropy for a given network structure, providing a new
theoretical tool with applications to social, technological and communication
networks.Comment: 4 pages (APS format), 3 figures, 1 tabl
Who Contributes to the Knowledge Sharing Economy?
Information sharing dynamics of social networks rely on a small set of
influencers to effectively reach a large audience. Our recent results and
observations demonstrate that the shape and identity of this elite, especially
those contributing \emph{original} content, is difficult to predict.
Information acquisition is often cited as an example of a public good. However,
this emerging and powerful theory has yet to provably offer qualitative
insights on how specialization of users into active and passive participants
occurs.
This paper bridges, for the first time, the theory of public goods and the
analysis of diffusion in social media. We introduce a non-linear model of
\emph{perishable} public goods, leveraging new observations about sharing of
media sources. The primary contribution of this work is to show that
\emph{shelf time}, which characterizes the rate at which content get renewed,
is a critical factor in audience participation. Our model proves a fundamental
\emph{dichotomy} in information diffusion: While short-lived content has simple
and predictable diffusion, long-lived content has complex specialization. This
occurs even when all information seekers are \emph{ex ante} identical and could
be a contributing factor to the difficulty of predicting social network
participation and evolution.Comment: 15 pages in ACM Conference on Online Social Networks 201
Epidemic processes in complex networks
In recent years the research community has accumulated overwhelming evidence
for the emergence of complex and heterogeneous connectivity patterns in a wide
range of biological and sociotechnical systems. The complex properties of
real-world networks have a profound impact on the behavior of equilibrium and
nonequilibrium phenomena occurring in various systems, and the study of
epidemic spreading is central to our understanding of the unfolding of
dynamical processes in complex networks. The theoretical analysis of epidemic
spreading in heterogeneous networks requires the development of novel
analytical frameworks, and it has produced results of conceptual and practical
relevance. A coherent and comprehensive review of the vast research activity
concerning epidemic processes is presented, detailing the successful
theoretical approaches as well as making their limits and assumptions clear.
Physicists, mathematicians, epidemiologists, computer, and social scientists
share a common interest in studying epidemic spreading and rely on similar
models for the description of the diffusion of pathogens, knowledge, and
innovation. For this reason, while focusing on the main results and the
paradigmatic models in infectious disease modeling, the major results
concerning generalized social contagion processes are also presented. Finally,
the research activity at the forefront in the study of epidemic spreading in
coevolving, coupled, and time-varying networks is reported.Comment: 62 pages, 15 figures, final versio
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