Complex Systems Studies

💰 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.

💰 Looking for a #PhD in Deep Learning Models for Human Behaviours? Come and join our group FBK https://t.co/Nn0eMyfz1R

🖥 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

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: http://www.mkivela.com/postdoc/

Three seminars on thermodynamics and machine learning. APS Data Science https://t.co/bO8ryaN75f

💰 #PhD Student in Hybrid Algorithms: Combining Deep Learning and Physical Models https://jobs.ethz.ch/job/view/JOPG_ethz_ajGObJpwqsyDc2qXsS

"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

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

The why, how, and when of representations for complex systems Leo Torres, Ann S. Blevins, Danielle S. Bassett, Tina Eliassi-Rad Download PDF 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.

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

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

Networks beyond pairwise interactions: structure and dynamics Federico Battiston, Giulia Cencetti, Iacopo Iacopini, Vito Latora, Maxime Lucas, Alice Patania, Jean-Gabriel Young, Giovanni Petri 🔗 Download PDF 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.

📺 https://youtu.be/AbwA_yupBkM

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.

"Beyond Networks: The Evolution of Living Systems": A very interesting lecture on evolution, ergodicity and predictability. From Laplace's hyper-deterministic views to Kauffman's non-ergodic universe and the adjacent possible (and more) https://youtu.be/sTXBFT4Ptkk https://www.aparat.com/v/PFR0A

What makes a network complex? In https://t.co/9r3GoZih0R we show that many properties associated to complex networks are recovered by thresholding normally distributed data.

💡 Node percolation for networks: A C-implementation + Python wrapper of Newman & Ziff's algorithm https://t.co/p3gVwtUQK4

💡 A forward-looking chapter by Mason Porter, "Nonlinearity + Networks: A 2020 Vision": https://t.co/eLdlD45oe8 It appears in the book "Emerging Frontiers in Nonlinear Science" (https://t.co/IBGX7bMyeA)

we can model a lightning strike by finding the shortest path in a random maze, from a point at the top to the ground. To find the path, we send out a frontier through the maze, and trace it back once it reaches the ground https://t.co/CsOsQ39hwS