🔖 Statistical physics of complex information dynamics

Arsham Ghavasieh, Carlo Nicolini, and Manlio De Domenico
Phys. Rev. E 102, 052304

Abstract
The constituents of a complex system exchange information to function properly. Their signaling dynamics often leads to the appearance of emergent phenomena, such as phase transitions and collective behaviors. While information exchange has been widely modeled by means of distinct spreading processes—such as continuous-time diffusion, random walks, synchronization and consensus—on top of complex networks, a unified and physically grounded framework to study information dynamics and gain insights about the macroscopic effects of microscopic interactions is still eluding us. In this paper, we present this framework in terms of a statistical field theory of information dynamics, unifying a range of dynamical processes governing the evolution of information on top of static or time-varying structures. We show that information operators form a meaningful statistical ensemble and their superposition defines a density matrix that can be used for the analysis of complex dynamics. As a direct application, we show that the von Neumann entropy of the ensemble can be a measure of the functional diversity of complex systems, defined in terms of the functional differentiation of higher-order interactions among their components. Our results suggest that modularity and hierarchy, two key features of empirical complex systems—from the human brain to social and urban networks—play a key role to guarantee functional diversity and, consequently, are favored.

🎞 Dirk Brockmann gave the keynote lecture on "A network perspective on the COVID-19 pandemic".

https://youtu.be/0XBIldY8agA?list=PLX5MLZUBu5RXLhBvhWBRc5ZxjXIYAnWJu

💡💉 چه کسانی‌ را ابتدا باید واکسینه کرد؟
https://www.wired.com/story/covid-19-vaccine-super-spreaders/

⛱ https://www.nytimes.com/2020/11/09/science/what-makes-sand-soft.html

💡 David Wolpert linking non-equilibrium physics and Bayesian networks

“Now we can start to analyze how thermodynamics of systems from cells to digital circuits depend on network structures connecting subsystems of those systems

https://t.co/pF8YbThaY2

Complex Systems Seminar Special Event | Searching for the densest network that does not always synchronize
Steven Strogatz, Applied Mathematics, Cornell University

Tuesday, November 17, 2020
2:30-4:00 PM
Virtual

https://lsa.umich.edu/cscs/news-events/all-events.detail.html/78499-20052321.html

Consistency and identifiability of football teams: a network science perspective

https://www.nature.com/articles/s41598-020-76835-3

💰 Interested in doing population-scale social network analysis? There are now two #PhD positions and one #Postdoc position in computational social sciences available @UvA_Amsterdam! Deadline is December 3.

https://t.co/HkUf8ObZeg

Using agent-based simulations, it shows that two network intervention strategies that divide or balance social groups can substantially reduce SARS-CoV-2 transmission while preserving the economy. https://t.co/3jGVV4OSM4

Introduction to Linear Algebra for Applied Machine Learn

https://t.co/CUEArRBicW

🦠 “Why Switzerland became one of the world's worst coronavirus hotspots”

https://t.co/8Z7wOiWkCC

💻 A machine learning tool called diffusion-maps has been used to identify topological phase transitions in experimental data.

https://physicsworld.com/a/machine-learning-spots-topological-phase-transitions-in-experimental-data/

🦠 مردم بیشتر در چه مکان‌هایی به کرونا مبتلا شدند؟!

مقاله‌ای در نیچر گزارش میده که عمده ابتلاها در مکان‌های کوچیک مثل - به ترتیب - رستوران‌ها، باشگاه‌های ورزشی، کافه‌ها و هتل‌ها بوده. آمار‌ ابتلا در رستوران‌ها و غذاخوری‌های عمومی در صدر‌ جدول و ۴ برابر‌ بیشتر از بقیه بوده.

https://t.co/WO4E0PMcp9

This week's paper is a novel approach to describe an "individual" in Biology without relying on features like cell membrane, but instead on a mathematical formulation that takes into account the information propagated in time.

You can read the annotated paper by David's Krakauer et al here: https://t.co/QuPd7A8m65

Come join us to discuss how not to construct #functional #brain #networks! We will talk about nodes and links definitions.


17.11.2020 at 10AM EET
https://aalto.zoom.us/j/67072679004

How to attend the seminar? Check https://bit.ly/3euKzvE

💰 #PhD positions in Advanced Machine Learning at Cambridge
Application deadline: noon December 3, 2020.
Details about the application process can be found here:
https://t.co/2SwKfm9V8k

📺 https://m.youtube.com/watch?feature=emb_rel_pause&v=L6CqqlILBCI

💰 Networks, embeddings, dynamics. If that sounds exciting to you, and if you're also searching for a #postdoc, Come and work on a (super cool) project: https://t.co/xwc3jP5l0s

💰 Two interesting opportunities for #PhD/#Postdoc at the Informatics Institute, University of Amsterdam!
https://t.co/bEuil6N7KG

Check also: https://t.co/JrxtuMJDV9

👩🏻‍💻 https://brianzhang01.github.io/2018/04/distributions-with-sympy/

🦠 از کجا بفهمیم که داریم کرونا رو شکست میدیم؟

بر خلاف آمار روزانه‌ای که شبکه‌های خبری میدن، که اصلا معلوم نمی‌کنه قراره در آینده چه اتفاقی بیافته، در این ویدیو یاد می‌گیریم که
چطور از ریاضیات و نمودارهای لگاریتمی میشه کمک گرفت تا آینده‌ای که پیش رو داریم رو روشن‌تر ببینیم و قدرت تحلیل و تصمیم‌گیریمون رو بالا ببریم، نتایج تصمیمات مختلف کشورها رو به روشنی ببینیم و حدس بزنیم که «کی قراره کرونا رو شکست بدیم؟»

🔗 sitpor.org/1399/08/beating-covid-19

#ما_کرونا_را_شکست‌‌_می‌دهیم
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@sitpor
‌instagram.com/sitpor_media
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