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This volume constitutes the proceedings of NetSci-X 2020: the Sixth
International School and Conference on Network Science, which was
held in Tokyo, Japan, in January 2020. NetSci-X is the Network
Science Society's winter conference series that covers a wide
variety of interdisciplinary topics on networks. Participants come
from various fields, including (but not limited to): mathematics,
physics, computer science, social sciences, management and
marketing sciences, organization science, communication science,
systems science, biology, ecology, neuroscience, medicine, as well
as business. This volume consists of contributed papers that have
been accepted to NetSc-X 2020 through a rigorous peer review
process. Researchers, students, and professionals will gain
first-hand information about today's cutting-edge research frontier
of network science.
This volume constitutes the proceedings of NetSci-X 2020: the Sixth
International School and Conference on Network Science, which was
held in Tokyo, Japan, in January 2020. NetSci-X is the Network
Science Society's winter conference series that covers a wide
variety of interdisciplinary topics on networks. Participants come
from various fields, including (but not limited to): mathematics,
physics, computer science, social sciences, management and
marketing sciences, organization science, communication science,
systems science, biology, ecology, neuroscience, medicine, as well
as business. This volume consists of contributed papers that have
been accepted to NetSc-X 2020 through a rigorous peer review
process. Researchers, students, and professionals will gain
first-hand information about today's cutting-edge research frontier
of network science.
Many multiagent dynamics can be modeled as a stochastic process in
which the agents in the system change their state over time in
interaction with each other. The Gillespie algorithms are popular
algorithms that exactly simulate such stochastic multiagent
dynamics when each state change is driven by a discrete event, the
dynamics is defined in continuous time, and the stochastic law of
event occurrence is governed by independent Poisson processes. The
first main part of this volume provides a tutorial on the Gillespie
algorithms focusing on simulation of social multiagent dynamics
occurring in populations and networks. The authors clarify why one
should use the continuous-time models and the Gillespie algorithms
in many cases, instead of easier-to-understand discrete-time
models. The remainder of the Element reviews recent extensions of
the Gillespie algorithms aiming to add more reality to the model
(i.e., non-Poissonian cases) or to speed up the simulations. This
title is also available as open access on Cambridge Core.
Network science offers a powerful language to represent and study
complex systems composed of interacting elements - from the
Internet to social and biological systems. A Guide to Temporal
Networks presents recent theoretical and modelling progress in the
emerging field of temporally varying networks and provides
connections between the different areas of knowledge required to
address this multi-disciplinary subject. After an introduction to
key concepts on networks and stochastic dynamics, the authors guide
the reader through a coherent selection of mathematical and
computational tools for network dynamics. Perfect for students and
professionals, this book is a gateway to an active field of
research developing between the disciplines of applied mathematics,
physics and computer science, with applications in others including
social sciences, neuroscience and biology.This second edition
extensively expands upon the coverage of the first addition as the
authors expertly present recent theoretical and modelling progress
in the emerging field of temporal networks, providing the keys to
(and connections between) the different areas of knowledge required
to address this multi-disciplinary problem.
Network science offers a powerful language to represent and study
complex systems composed of interacting elements - from the
Internet to social and biological systems. In its standard
formulation, this framework relies on the assumption that the
underlying topology is static, or changing very slowly as compared
to dynamical processes taking place on it, e.g., epidemic spreading
or navigation. Fuelled by the increasing availability of
longitudinal networked data, recent empirical observations have
shown that this assumption is not valid in a variety of situations.
Instead, often the network itself presents rich temporal properties
and new tools are required to properly describe and analyse their
behaviour.A Guide to Temporal Networks presents recent theoretical
and modelling progress in the emerging field of temporally varying
networks, and provides connections between different areas of
knowledge required to address this multi-disciplinary subject.
After an introduction to key concepts on networks and stochastic
dynamics, the authors guide the reader through a coherent selection
of mathematical and computational tools for network dynamics.
Perfect for students and professionals, this book is a gateway to
an active field of research developing between the disciplines of
applied mathematics, physics and computer science, with
applications in others including social sciences, neuroscience and
biology.
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