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

https://www.aub.edu.lb/cams/Pages/Covid19.aspx

An online-conference as an occasion for 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.

📺 https://youtu.be/AbwA_yupBkM

Networks beyond pairwise interactions: structure and dynamics

Federico Battiston, Giulia Cencetti, Iacopo Iacopini, Vito Latora, Maxime Lucas, Alice Patania, Jean-Gabriel Young, Giovanni Petri

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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.

#PhD Please just drop me an email with your CV and one or two short paragraphs about your experience. Email https://t.co/GbOkb6VrBs

Advice to young scholars, Aaron Clauset

Panel 1. The Academic Job Market
Panel 2. Life / Work Balance
Panel 3. Interdisciplinary Research
Panel 4. Grants and Fundraising

The why, how, and when of representations for complex systems

Leo Torres, Ann S. Blevins, Danielle S. Bassett, Tina Eliassi-Rad

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Complex systems thinking is applied to a wide variety of domains, from neuroscience to computer science and economics. The wide variety of implementations has resulted in two key challenges: the progenation of many domain-specific strategies that are seldom revisited or questioned, and the siloing of ideas within a domain due to inconsistency of complex systems language. In this work we offer basic, domain-agnostic language in order to advance towards a more cohesive vocabulary. We use this language to evaluate each step of the complex systems analysis pipeline, beginning with the system and data collected, then moving through different mathematical formalisms for encoding the observed data (i.e. graphs, simplicial complexes, and hypergraphs), and relevant computational methods for each formalism. At each step we consider different types of \emph{dependencies}; these are properties of the system that describe how the existence of one relation among the parts of a system may influence the existence of another relation. We discuss how dependencies may arise and how they may alter interpretation of results or the entirety of the analysis pipeline. We close with two real-world examples using coauthorship data and email communications data that illustrate how the system under study, the dependencies therein, the research question, and choice of mathematical representation influence the results. We hope this work can serve as an opportunity of reflection for experienced complexity scientists, as well as an introductory resource for new researchers.

به طور خلاصه مقاله ‌مروری بالا در مورد اینه که در دهه پیش رو بنیادهای علم شبکه چه شکلی خواهد بود!

"Few-body and many-body chaos" with Vladimir Rosenhaus:

June 4 lecture: https://youtu.be/onTWbF5fVyM
June 5 lecture: https://youtu.be/-EE24Bi77OQ


See here for slides and other details:
https://itsatcuny.org/summerschool/rosenhaus-lecture

💰 #PhD Student in Hybrid Algorithms: Combining Deep Learning and Physical Models

https://jobs.ethz.ch/job/view/JOPG_ethz_ajGObJpwqsyDc2qXsS

Three seminars on thermodynamics and machine learning.
APS Data Science

https://t.co/bO8ryaN75f

Want to analyse multilayer network data and develop network models in my group at CS Aalto in Finland? I have a #postdoc position open in an interdisciplinary project on climate change communication, polarisation and more.

Full information here: https://www.mkivela.com/postdoc/

🖥 وبینار: سیستم‌های دینامیکی در نظریه‌ی کنترل غیرخطی
👤 نسرین صدری
📋 پنجشنبه، ۲۲ خردادماه؛ ساعت ۱۱ تا ۱۲
📍‌لینک وبینار:
vmeeting.ipm.ir/b/isf-q2n-prq

#IPM
© @IPMMath
🆔 @Zharfa90

💰 Looking for a #PhD in Deep Learning Models for Human Behaviours? Come and join our group FBK

https://t.co/Nn0eMyfz1R

💰 Do you love temporal networks? How about dynamic embeddings? Are you a fan of deep learning sequence modeling? Then come work in Copenhagen on a super cool and nerdy #PhD Project: https://t.co/hNGohb4Ham.

Index of Complex Networks (ICON) was launched @netsci2016 and now lists 689 data sets and 5403 networks, spanning all domains of science and all types of networks, in a fully searchable index. https://t.co/QNmdXmWlfn

The Markov Chain Monte Carlo Revolution https://t.co/JgoF9qoh1x by Persi Diaconis

AI course, Machine Learning, based on the renowned Stanford CS course taught by Andrew Y Ng. The online course starts July 13th and runs for 10 weeks.

https://online.stanford.edu/courses/xcs229i-machine-learning

COVID-19 is breaking records for its speed of global domination – what are the changes in global connectivity behind this and how can we measure them?
Nice perspective by @AndyTatem
https://t.co/j35Vemv3Qd https://t.co/Tx3mmI2ifb