Notions and methods rooted in or with strong flavour of statistical physics that have been applied to study the
structural, dynamical or control properties of complex networks

Controlling complex networks with complex nodes
https://www.nature.com/articles/s42254-023-00566-3

Notions and methods from control theory that have been applied to analyse and control complex networks

Controlling complex networks with complex nodes
https://www.nature.com/articles/s42254-023-00566-3

We are looking for an enthusiastic and motivated candidate from the fields of computational biology, mathematics, computational physics, engineering, and similar for a #PhD position in a DFG-funded research project "Association between abundance structure and biodiversity patterns as a signature of deterministic processes".

https://www.linkedin.com/jobs/view/3517580042/?refId=PEsKJ8Ia%2FXOuXnOk21FlpA%3D%3D&trackingId=PEsKJ8Ia%2FXOuXnOk21FlpA%3D%3D

fully-funded #PhD student (up to 5 years) at the intersection of comp neuro 🧠 and ML 🤖

👉 sprekelerlab.org/jobs/

Unifying Pairwise Interactions in Complex Dynamics
Oliver M. Cliff, Joseph T. Lizier, Naotsugu Tsuchiya, Ben D. Fulcher
arxiv.org/abs/2201.11941


Scientists have developed hundreds of techniques to measure the interactions between pairs of processes in complex systems. But these computational methods -- from correlation coefficients to causal inference -- rely on distinct quantitative theories that remain largely disconnected. Here we introduce a library of 249 statistics for pairwise interactions and assess their behavior on 1053 multivariate time series from a wide range of real-world and model-generated systems. Our analysis highlights new commonalities between different mathematical formulations, providing a unified picture of a rich, interdisciplinary literature. We then show that leveraging many methods from across science can uncover those most suitable for addressing a given problem, yielding high accuracy and interpretable understanding. Our framework is provided in extendable open software, enabling comprehensive data-driven analysis by integrating decades of methodological advances.

#RA
RA position in gender and socioeconomic inequality in Bonn

https://twitter.com/MartaGolin/status/1646490390101475331?s=20

CMPLXSYS 351 - Introduction to Social Science Data
Spring 2023, Section 101

https://www.lsa.umich.edu/cg/cg_detail.aspx?content=2430CMPLXSYS351101&termArray=sp_23_2430#ClassTextbooks

Postdoctoral position in Computer Science with a focus on Professional Competences and Equal Opportunities

https://www.uu.se/en/about-uu/join-us/details/?positionId=607358

We are looking for a #postdoc research associate to study physics of elastic turbulence at the School of Physics & Astronomy, University of Edinburgh, UK. Candidates with experience in fluid dynamics of Newtonian and complex fluids, high-performance computing, and track record in transition to turbulence, coherent structures, and dynamical systems are strongly encouraged to apply. For further details, please visit: tinyurl.com/3kh9etsj. Deadline: May 10, 2023.

#PhD
Looking for a PhD in computational social sciences ? We are launching a call for application for a PhD Grant starting in 2023 on opinion dynamics and social networks evolution
@ISCPIF

Under the co-direction of
@AraluHernandez
Details on
https://twitter.com/chavalarias/status/1648723785002917888?s=20

The Unreasonable Effectiveness of Contact Tracing on Networks with Cliques

🔗 arxiv.org/abs/2304.10405

Contact tracing, the practice of isolating individuals who have been in contact with infected individuals, is an effective and practical way of containing disease spread. Here, we show that this strategy is particularly effective in the presence of social groups: Once the disease enters a group, contact tracing not only cuts direct infection paths but can also pre-emptively quarantine group members such that it will cut indirect spreading routes. We show these results by using a deliberately stylized model that allows us to isolate the effect of contact tracing within the clique structure of the network where the contagion is spreading. This will enable us to derive mean-field approximations and epidemic thresholds to demonstrate the efficiency of contact tracing in social networks with small groups. This analysis shows that contact tracing in networks with groups is more efficient the larger the groups are. We show how these results can be understood by approximating the combination of disease spreading and contact tracing with a complex contagion process where every failed infection attempt will lead to a lower infection probability in the next attempts. Our results illustrate how contract tracing in real-world settings can be more efficient than predicted by models that treat the system as fully mixed or the network structure as locally tree-like.

🧵Check out this thread: https://threadreaderapp.com/thread/1649344692771778560.html

Information Theory for Complex Systems Scientists
Thomas F. Varley

https://arxiv.org/abs/2304.12482

Lectures on Statistical Physics

http://sgovindarajan.wikidot.com/statistical-physics

IMPRS #PhD program is only accepting applications for another 5️⃣ days.

👉imprs-mis.mpg.de

Workshop on Stochastic Thermodynamics

https://indico.ictp.it/event/10171

Kobe University's Center for Computational Social Science is hosting an online symposium on behavioral and computational social science on May 8th (JST).

http://ccss.kobe-u.ac.jp/event/seminar_all/2023/202305081630.html

A comprehensive review of signal propagation in complex networks:
https://doi.org/10.1016/j.physrep.2023.03.005