A digression: Someone asked me about my Rodney King and Kent
State mention last week.
I'm sure Wikipedia and Google would have helped him, but here is a
quick summary:
They're from a previous generation, and many of you are somewhat
younger ;-)
Rodney King was badly beaten by Los Angeles cops --an even that
was video'd and widely broadcast. It was the occasion for widely
spread angry protests in LA and elsewhere.
The Killings at Kent state was when National Guard soldiers killed
several students engaging in peaceful anti-war protests at Kent
State University.
And so it goes.... Everything old is new again.
-------- Forwarded Message --------
Learn about the latest and greatest related to complex systems
research. More at
https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=9127765128&e=55e25a0e3e
Networks beyond pairwise interactions: structure and dynamics
Federico Battiston, Giulia Cencetti, Iacopo Iacopini, Vito Latora,
Maxime Lucas, Alice Patania, Jean-Gabriel Young, Giovanni Petri
The complexity of many biological, social and technological
systems stems from the richness of the interactions among their
units. Over the past decades, a great variety of complex systems
has been successfully described as networks whose interacting
pairs of nodes are connected by links. Yet, in face-to-face human
communication, chemical reactions and ecological systems,
interactions can occur in groups of three or more nodes and cannot
be simply described just in terms of simple dyads. Until recently,
little attention has been devoted to the higher-order architecture
of real complex systems. However, a mounting body of evidence is
showing that taking the higher-order structure of these systems
into account can greatly enhance our modeling capacities and help
us to understand and predict their emerging dynamical behaviors.
Here, we present a complete overview of the emerging field of
networks beyond pairwise interactions. We first discuss the
methods to represent higher-order interactions
and give a unified presentation of the different frameworks used
to describe higher-order systems, highlighting the links between
the existing concepts and representations. We review the measures
designed to characterize the structure of these systems and the
models proposed in the literature to generate synthetic
structures, such as random and growing simplicial complexes,
bipartite graphs and hypergraphs. We introduce and discuss the
rapidly growing research on higher-order dynamical systems and on
dynamical topology. We focus on novel emergent phenomena
characterizing landmark dynamical processes, such as diffusion,
spreading, synchronization and games, when extended beyond
pairwise interactions. We elucidate the relations between
higher-order topology and dynamical properties, and conclude with
a summary of empirical applications, providing an outlook on
current modeling and conceptual frontiers.
Source: arxiv.org
(
https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=27e4ced57c&e=55e25a0e3e)
Universal evolution patterns of degree assortativity in social
networks
https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=7db62b396e&e=55e25a0e3e
Bin Zhou, Xin Lu, Petter Holme
Social Networks
Volume 63, October 2020, Pages 47-55
• A universal rise-and-fall pattern for assortativity is found in
empirical networks
• The bidirectional selection model can re-construct the evolution
of assortativity
• Heterogeneity of social status may drive the network evolution
towards self-optimization
• The social status gap plays an important role for the evolution
of network assortativity
Source:
www.sciencedirect.com
(
https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=b4e272f056&e=55e25a0e3e)
On Assessing Control Actions for Epidemic Models on Temporal
Networks
Lorenzo Zino ; Alessandro Rizzo ; Maurizio Porfiri
IEEE Control Systems Letters 4(4)
In this letter, we propose an epidemic model over temporal
networks that explicitly encapsulates two different control
actions. We develop our model within the theoretical framework of
activity driven networks (ADNs), which have emerged as a valuable
tool to capture the complexity of dynamical processes on networks,
coevolving at a comparable time scale to the temporal network
formation. Specifically, we complement a
susceptible–infected–susceptible epidemic model with features that
are typical of nonpharmaceutical interventions in public health
policies: i) actions to promote awareness, which induce people to
adopt self-protective behaviors, and ii) confinement policies to
reduce the social activity of infected individuals. In the
thermodynamic limit of large-scale populations, we use a
mean-field approach to analytically derive the epidemic threshold,
which offers viable insight to devise containment actions at the
early stages of the outbreak. Through the proposed model, it is
possible
to devise an optimal epidemic control policy as the combination of
the two strategies, arising from the solution of an optimization
problem. Finally, the analytical computation of the epidemic
prevalence in endemic diseases on homogeneous ADNs is used to
optimally calibrate control actions toward mitigating an endemic
disease. Simulations are provided to support our theoretical
results.
Source: ieeexplore.ieee.org
(
https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=33d3f52776&e=55e25a0e3e)
Networked Complexity: The Case of COVID-19. June 8-11, 2020
https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=53d2d8c80e&e=55e25a0e3e
Close monitoring of the COVID-19 pandemic provides a blow by blow
account of a spatio-temporal process percolating over complex
(social)-networks. Efforts to contain the spread of the disease
were and remain, for better or worse, explicitly informed by a
rich tradition of mathematical models of such processes. This
tradition was further enriched in the past couple of decades with
the emergence of globally networked virtual societies, and the
deployment of fine grained networks of sensors, both enabling the
gathering of highly resolved data on the structure of complex
networks, and flows over them.
Our online-conference is an occasion for expert reviews of this
tradition, then presentations of work-in-progress on the gathering
of epidemiological data (technical and ethical challenges), and
its modeling (from the coarse grained compartmental, to the fine
grained agent based models), with the urgency of COVID-19
mitigation in the air.
Taking place as it does at a cusp in a global pandemic, the
meeting is for us at CAMS a timely intervention in a collaboration
with the National Center for Remote Sensing (NCRS, CNRS-L) the
principle aim of which is to harness big data analytics and
complexity theory at the service of national and regional
priorities. It draws on local expertise in concerned disciplines
(in this case: physics, biology, epidemiology and sociology), and
contributions by experts at leading international laboratories in
data analytics, and complexity science (e.g. Multiscale and
Quantum Physics, Aalto University, Finland; The Bartlett Center
for Advanced Spatial Analysis, UCL, London; Center of Complexity
Sciences (C3), UNAM, Mexico; The Alan Turing Institute, London;
ICTP, Trieste, Italy; etc.).
Source:
www.aub.edu.lb
(
https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=740ab5f3f0&e=55e25a0e3e)
Performing Complexity: Building Foundations for the Practice of
Complex Thinking | Ana Teixeira de Melo
https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=735dc2024c&e=55e25a0e3e
In the face of growing challenges, we need modes of thinking that
allow us to not only grasp complexity but also perform it. In this
book, the author approaches complexity from the standpoint of a
relational worldview. The author recasts complex thinking as a
mode of coupling between an observer and the world. Further, she
explores the process and outcome of that coupling, namely,
meaningful information that may have transformative effects and
impact the management of change in the ‘real world’. The author
presents a new framework for operationalising complex thinking in
a set of dimensions and properties through which it may be
enacted. This framework may inform the development and
coordination of new tools and strategies to support the practice
and evaluation of complex thinking across a variety of domains.
Intended for a wide interdisciplinary audience of academics,
practitioners and policymakers alike, the book is an invitation to
pursue inter- and transdisciplinary dialogues and
collaborations.
Source:
www.springer.com
(
https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=95bb34a7a3&e=55e25a0e3e)
A network analysis of research productivity by country,
discipline, and wealth
Jaffe K, ter Horst E, Gunn LH, Zambrano JD, Molina G (2020) A
network analysis of research productivity by country, discipline,
and wealth. PLoS ONE 15(5): e0232458.
https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=09a5d1efa7&e=55e25a0e3e
** Introduction
------------------------------------------------------------
Research productivity has been linked to a country’s intellectual
and economic wealth. Further analysis is needed to assess the
association between the distribution of research across
disciplines and the economic status of countries.
** Methods
------------------------------------------------------------
By using 55 years of data, spanning 1962 to 2017, of Elsevier
publications across a large set of research disciplines and
countries globally, this manuscript explores the relationship and
evolution of relative research productivity across different
disciplines through a network analysis. It also explores the
associations of those with economic productivity categories, as
measured by the World Bank economic classification. Additional
analysis of discipline similarities is possible by exploring the
cross-country evolution of those disciplines.
** Results
------------------------------------------------------------
Results show similarities in the relative importance of research
disciplines among most high-income countries, with larger
idiosyncrasies appearing among the remaining countries. This group
of high-income countries shows similarities in the dynamics of the
relative distribution of research productivity over time, forming
a stable research productivity cluster. Lower income countries
form smaller, more independent and evolving clusters, and differ
significantly from each other and from higher income countries in
the relative importance of their research emphases. Country-based
similarities in research productivity profiles also appear to be
influenced by geographical proximity.
** Conclusions
------------------------------------------------------------
This new form of analyses of research productivity, and its
relation to economic status, reveals novel insights to the
dynamics of the economic and research structure of countries. This
allows for a deeper understanding of the role a country’s research
structure may play in shaping its economy, and also identification
of benchmark resource allocations across disciplines for
developing countries.
Source: journals.plos.org
(
https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=c8fb853420&e=55e25a0e3e)
Postdoctoral Fellow, Socioeconomic patterns in network formation
and mobility | Central European University
The Department of Network and Data Science (DNDS) at the Central
European University (CEU) carries out research in network science,
with a special focus on the foundations and applications of
network science to practical data-driven problems. A key element
of the mission of DNDS is to work across disciplines to bring
network and data science tools to many fields of the social
sciences and related areas. DNDS translates these ideas into
research projects - our faculty have won several major grants,
from European Union and US funding agencies. DNDS offers a PhD
Program and an Advanced Certificate Program in Network Science and
will host a BA in Quantitative Social Sciences starting,
presumably, in 2021. Data science tools and the network science
approach offer a unique perspective to tackle complex problems,
impenetrable to linear-proportional thinking. Building on decades
of development of fundamental understanding of networks, the
modern data deluge has opened up unprecedented
opportunities to study and understand the structure and function
of social, economic, political and information systems.
Data-driven network science aims at explaining complex phenomena
at larger scales emerging from simple principles of network link
formation.
Source:
www.ceu.edu
(
https://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=f548919a14&e=55e25a0e3e)
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Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.
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