📺 video

Graphs + Networks Workshop materials that include videos of all the talks from the conference.

Here is a list of papers that our speakers and participants flagged as interesting for wider reading in Graphs, Networks, and meaningful applications.

علم سیستم‌های پیچیده
https://www.aparat.com/v/veRud

💰 Jobs: Up to 5 #postdoc positions in Computational Social Science (with focus on AI)

Join me at the Max-Planck Center for Humans & Machines in Berlin

https://t.co/ay7o7uzKOM

The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains

David I Shuman, Sunil K. Narang, Pascal Frossard, Antonio Ortega, Pierre Vandergheynst

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In applications such as social, energy, transportation, sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs. The emerging field of signal processing on graphs merges algebraic and spectral graph theoretic concepts with computational harmonic analysis to process such signals on graphs. In this tutorial overview, we outline the main challenges of the area, discuss different ways to define graph spectral domains, which are the analogues to the classical frequency domain, and highlight the importance of incorporating the irregular structures of graph data domains when processing signals on graphs. We then review methods to generalize fundamental operations such as filtering, translation, modulation, dilation, and downsampling to the graph setting, and survey the localized, multiscale transforms that have been proposed to efficiently extract information from high-dimensional data on graphs. We conclude with a brief discussion of open issues and possible extensions.

Unraveling the effects of multiscale network entanglement on disintegration of empirical systems

Arsham Ghavasieh, Massimo Stella, Jacob Biamonte, Manlio De Domenico

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Complex systems are large collections of entities that organize themselves into non-trivial structures that can be represented by networks. A key emergent property of such systems is robustness against random failures or targeted attacks ---i.e. the capacity of a network to maintain its integrity under removal of nodes or links. Here, we introduce network entanglement to study network robustness through a multi-scale lens, encoded by the time required to diffuse information through the system. Our measure's foundation lies upon a recently proposed framework, manifestly inspired by quantum statistical physics, where networks are interpreted as collections of entangled units and can be characterized by Gibbsian-like density matrices. We show that at the smallest temporal scales entanglement reduces to node degree, whereas at the large scale we show its ability to measure the role played by each node in network integrity. At the meso-scale, entanglement incorporates information beyond the structure, such as system's transport properties. As an application, we show that network dismantling of empirical social, biological and transportation systems unveils the existence of a optimal temporal scale driving the network to disintegration. Our results open the door for novel multi-scale analysis of network contraction process and its impact on dynamical processes.

🦠 “If schools are reopened in areas with high levels of community transmission, major outbreaks are inevitable and deaths will occur in the community as a result."

#coronavirus #covid19
https://www.nature.com/articles/d41586-020-02403-4

Swiss Government Excellence Scholarships for Foreign Scholars and Artists for the 2021-2022 Academic Year

For Iran, see here

#phd #postdoc

Want to learn more about agent-based modeling applied to social-ecological systems? Apply to our winter school!

https://t.co/nMbKvhplgu

#PhD positions are available at IPM in Cognitive Neuroscience. To apply see the info on:
https://scs.ipm.ac.ir

💡 A simple but powerful idea: Improve the throughput of pooled testing for COVID-19 using a (Reed-Solomon) error-correcting code.
https://t.co/21CTet1b45

💡 "Nonlocal Diffusion Equations with Integrable Kernels" (by Julio D. Rossi): https://t.co/wjvXc68RNV

📺 the first video in a new series on sparsity and compression.

https://t.co/Z4kjonwkgE

چگونه با آمار دروغ بگوییم؟

معرفی، مختصر توضیحی و دعوتی برای مطالعه کتاب «چگونه با آمار دروغ بگوییم؟»

🔗 https://www.sitpor.org/2020/08/how-to-lies-with-statistics/

📈📊📉
@sitpor

♾ https://www.pnas.org/content/104/42/16432.full

🦠 https://time.com/5883081/covid-19-transmitted-aerosols

💡 "Introducing students to research codes: A short course on solving partial differential equations in Python"

(by Pavan Inguva, Vijesh J. Bhute, Thomas N.H. Cheng, Pierre J. Walker)

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Recent releases of open-source research codes and solvers for numerically solving partial differential equations in Python present a great opportunity for educators to integrate these codes into the classroom in a variety of ways. The ease with which a problem can be implemented and solved using these codes reduce the barrier to entry for users. We demonstrate how one of these codes,FiPy, can be introduced to students through a short course using progression as the guiding philosophy. Four exercises of increasing complexity were developed. Basic concepts from more advanced numerical methods courses are also introduced at appropriate points. To further engage students, we demonstrate how an open research problem can be readily implemented and also incorporate the use of ParaView to post-process their results. Student engagement and learning outcomes were evaluated through a pre and post-course survey and a focus group discussion. Students broadly found the course to be engaging and useful with the ability to easily visualise the solution to PDEs being greatly valued. Due to the introductory nature of the course, due care in terms of set-up and the design of learning activities during the course is essential. This course, if integrated with appropriate level of support, can encourage students to use the provided codes and improve their understanding of concepts used in numerical analysis and PDEs.

Why, even during lockdown, do #coronavirus infection curves continue to grow linearly? The answer lies in networks.

"For any given #transmission rate there exists a critical degree of contact #networks below which linear #infection curves must occur and above which the classical S-shaped curves appear that are known from epidemiological models."

https://t.co/j70KwyijkW

💡 Mongolia’s pandemic response has been so effective that nobody has died of covid-19 there, despite the long border with China. An epidemiologist there explains how the country has kept the virus at bay without a great public health system.

https://t.co/mMDFz05Psa

Computational Methods for Complex Systems
Instructor: Chris Myers

Computational science and engineering involves the synthesis of data structures, algorithms, numerical analysis, programming methodologies, simulation, visualization, data analysis, performance optimization, and use of emerging technologies, all applied to the study of complex problems in science and engineering. Physics 7682 is a graduate computational science laboratory course, emphasizing hands-on programming to address a number of interesting problems arising in physics, biology, engineering, applied mathematics, and computer science. The course is largely self-paced, allowing students to choose from among a variety of topics, and explore new problems of particular interest. Unlike other courses focused more specifically on algorithms, data structures or numerical analysis, this course emphasizes the integration of those and other topics to understand a variety of scientific phenomena and computational methods.

Computer Exercises

Topics
The course is organized around computational modules, which are drawn from a number of different fields. The course originally was a core element in the curriculum of Cornell's IGERT Program in Nonlinear Systems, which was organized broadly around the themes of complex networks, biolocomotion and manipulation, gene regulation, and pattern formation. Topics have evolved organically from that starting point. Modules are designed to expose students to techniques and methods from a variety of disciplines, not normally encompassed in a single course. Computational methods include solution of ordinary and partial differential equations, root finding, graph traversal, stochastic simulation via Monte Carlo, and various techniques in data analysis. Scientific topics include:

Complex networks, small worlds, and percolation
Human locomotion and models of walking
Dynamical systems, chaos, and iterated maps
Pattern formation and spiral waves in cardiac tissue
Chemical kinetics and gene regulatory networks
Random matrix theory
Random walks, extremal statistics, and stock fluctuations
Lattice Monte Carlo and the Ising model
Satisfiability and phase transitions in NP-complete problems
Molecular dynamics and the emergence of thermodynamics

https://pages.physics.cornell.edu/~myers/teaching/ComputationalMethods/index.html