1,626 research outputs found
Spreading processes in Multilayer Networks
Several systems can be modeled as sets of interconnected networks or networks
with multiple types of connections, here generally called multilayer networks.
Spreading processes such as information propagation among users of an online
social networks, or the diffusion of pathogens among individuals through their
contact network, are fundamental phenomena occurring in these networks.
However, while information diffusion in single networks has received
considerable attention from various disciplines for over a decade, spreading
processes in multilayer networks is still a young research area presenting many
challenging research issues. In this paper we review the main models, results
and applications of multilayer spreading processes and discuss some promising
research directions.Comment: 21 pages, 3 figures, 4 table
Urban characteristics attributable to density-driven tie formation
Motivated by empirical evidence on the interplay between geography,
population density and societal interaction, we propose a generative process
for the evolution of social structure in cities. Our analytical and simulation
results predict both super-linear scaling of social tie density and information
flow as a function of the population. We demonstrate that our model provides a
robust and accurate fit for the dependency of city characteristics with city
size, ranging from individual-level dyadic interactions (number of
acquaintances, volume of communication) to population-level variables
(contagious disease rates, patenting activity, economic productivity and crime)
without the need to appeal to modularity, specialization, or hierarchy.Comment: Early version of this paper was presented in NetSci 2012 as a
contributed talk in June 2012. An improved version of this paper is published
in Nature Communications in June 2013. It has 14 pages and 5 figure
Analysis of complex contagions in random multiplex networks
We study the diffusion of influence in random multiplex networks where links
can be of different types, and for a given content (e.g., rumor, product,
political view), each link type is associated with a content dependent
parameter in that measures the relative bias type- links
have in spreading this content. In this setting, we propose a linear threshold
model of contagion where nodes switch state if their "perceived" proportion of
active neighbors exceeds a threshold \tau. Namely, a node connected to
active neighbors and inactive neighbors via type- links will turn
active if exceeds its threshold \tau. Under this
model, we obtain the condition, probability and expected size of global
spreading events. Our results extend the existing work on complex contagions in
several directions by i) providing solutions for coupled random networks whose
vertices are neither identical nor disjoint, (ii) highlighting the effect of
content on the dynamics of complex contagions, and (iii) showing that
content-dependent propagation over a multiplex network leads to a subtle
relation between the giant vulnerable component of the graph and the global
cascade condition that is not seen in the existing models in the literature.Comment: Revised 06/08/12. 11 Pages, 3 figure
Multilayer Networks
In most natural and engineered systems, a set of entities interact with each
other in complicated patterns that can encompass multiple types of
relationships, change in time, and include other types of complications. Such
systems include multiple subsystems and layers of connectivity, and it is
important to take such "multilayer" features into account to try to improve our
understanding of complex systems. Consequently, it is necessary to generalize
"traditional" network theory by developing (and validating) a framework and
associated tools to study multilayer systems in a comprehensive fashion. The
origins of such efforts date back several decades and arose in multiple
disciplines, and now the study of multilayer networks has become one of the
most important directions in network science. In this paper, we discuss the
history of multilayer networks (and related concepts) and review the exploding
body of work on such networks. To unify the disparate terminology in the large
body of recent work, we discuss a general framework for multilayer networks,
construct a dictionary of terminology to relate the numerous existing concepts
to each other, and provide a thorough discussion that compares, contrasts, and
translates between related notions such as multilayer networks, multiplex
networks, interdependent networks, networks of networks, and many others. We
also survey and discuss existing data sets that can be represented as
multilayer networks. We review attempts to generalize single-layer-network
diagnostics to multilayer networks. We also discuss the rapidly expanding
research on multilayer-network models and notions like community structure,
connected components, tensor decompositions, and various types of dynamical
processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure
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