26,218 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
The physics of spreading processes in multilayer networks
The study of networks plays a crucial role in investigating the structure,
dynamics, and function of a wide variety of complex systems in myriad
disciplines. Despite the success of traditional network analysis, standard
networks provide a limited representation of complex systems, which often
include different types of relationships (i.e., "multiplexity") among their
constituent components and/or multiple interacting subsystems. Such structural
complexity has a significant effect on both dynamics and function. Throwing
away or aggregating available structural information can generate misleading
results and be a major obstacle towards attempts to understand complex systems.
The recent "multilayer" approach for modeling networked systems explicitly
allows the incorporation of multiplexity and other features of realistic
systems. On one hand, it allows one to couple different structural
relationships by encoding them in a convenient mathematical object. On the
other hand, it also allows one to couple different dynamical processes on top
of such interconnected structures. The resulting framework plays a crucial role
in helping achieve a thorough, accurate understanding of complex systems. The
study of multilayer networks has also revealed new physical phenomena that
remain hidden when using ordinary graphs, the traditional network
representation. Here we survey progress towards attaining a deeper
understanding of spreading processes on multilayer networks, and we highlight
some of the physical phenomena related to spreading processes that emerge from
multilayer structure.Comment: 25 pages, 4 figure
Modeling Infection with Multi-agent Dynamics
Developing the ability to comprehensively study infections in small
populations enables us to improve epidemic models and better advise individuals
about potential risks to their health. We currently have a limited
understanding of how infections spread within a small population because it has
been difficult to closely track an infection within a complete community. The
paper presents data closely tracking the spread of an infection centered on a
student dormitory, collected by leveraging the residents' use of cellular
phones. The data are based on daily symptom surveys taken over a period of four
months and proximity tracking through cellular phones. We demonstrate that
using a Bayesian, discrete-time multi-agent model of infection to model
real-world symptom reports and proximity tracking records gives us important
insights about infec-tions in small populations
On degree-degree correlations in multilayer networks
We propose a generalization of the concept of assortativity based on the
tensorial representation of multilayer networks, covering the definitions given
in terms of Pearson and Spearman coefficients. Our approach can also be applied
to weighted networks and provides information about correlations considering
pairs of layers. By analyzing the multilayer representation of the airport
transportation network, we show that contrasting results are obtained when the
layers are analyzed independently or as an interconnected system. Finally, we
study the impact of the level of assortativity and heterogeneity between layers
on the spreading of diseases. Our results highlight the need of studying
degree-degree correlations on multilayer systems, instead of on aggregated
networks.Comment: 8 pages, 3 figure
The role of caretakers in disease dynamics
One of the key challenges in modeling the dynamics of contagion phenomena is
to understand how the structure of social interactions shapes the time course
of a disease. Complex network theory has provided significant advances in this
context. However, awareness of an epidemic in a population typically yields
behavioral changes that correspond to changes in the network structure on which
the disease evolves. This feedback mechanism has not been investigated in
depth. For example, one would intuitively expect susceptible individuals to
avoid other infecteds. However, doctors treating patients or parents tending
sick children may also increase the amount of contact made with an infecteds,
in an effort to speed up recovery but also exposing themselves to higher risks
of infection. We study the role of these caretaker links in an adaptive network
models where individuals react to a disease by increasing or decreasing the
amount of contact they make with infected individuals. We find that pure
avoidance, with only few caretaker links, is the best strategy for curtailing
an SIS disease in networks that possess a large topological variability. In
more homogeneous networks, disease prevalence is decreased for low
concentrations of caretakers whereas a high prevalence emerges if caretaker
concentration passes a well defined critical value.Comment: 8 pages, 9 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
Complex Agent Networks explaining the HIV epidemic among homosexual men in Amsterdam
Simulating the evolution of the Human Immunodeficiency Virus (HIV) epidemic
requires a detailed description of the population network, especially for small
populations in which individuals can be represented in detail and accuracy. In
this paper, we introduce the concept of a Complex Agent Network(CAN) to model
the HIV epidemics by combining agent-based modelling and complex networks, in
which agents represent individuals that have sexual interactions. The
applicability of CANs is demonstrated by constructing and executing a detailed
HIV epidemic model for men who have sex with men (MSM) in Amsterdam, including
a distinction between steady and casual relationships. We focus on MSM contacts
because they play an important role in HIV epidemics and have been tracked in
Amsterdam for a long time. Our experiments show good correspondence between the
historical data of the Amsterdam cohort and the simulation results.Comment: 21 pages, 4 figures, Mathematics and Computers in Simulation, added
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