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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 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.


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
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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 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

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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.).

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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 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.

** 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.

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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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