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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 
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 
And so it goes.... Everything old is new again.


Barry Wellman, FRSC               Director, NetLab Network
Founder, International Network for Social Network Analysis

Bit by bit, putting it together--Sondheim
It's Always Something--Roseanne Roseannadanna

Getting It Done; Getting It Out: A Practical Guide to Writing, Editing, Presenting and Promoting in the Social Sciences--coming in 2021 (Guilford Press)

NETWORKED: The New Social Operating System  Lee Rainie & Barry Wellman    

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Subject: 	Latest Complexity Digest Posts
Date: 	Mon, 8 Jun 2020 11:02:42 +0000
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Learn about the latest and greatest related to complex systems research. 
More at 

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.

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Universal evolution patterns of degree assortativity in social networks 

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 
• Heterogeneity of social status may drive the network evolution towards 
• The social status gap plays an important role for the evolution of 
network assortativity

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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.

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Networked Complexity: The Case of COVID-19. June 8-11, 2020 

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.).

( )

Performing Complexity: Building Foundations for the Practice of Complex 
Thinking | Ana Teixeira de Melo 

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

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A network analysis of research productivity by country, discipline, and 

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. 

** 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 

** 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.

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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.

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Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

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