Our paper “Generalized diffusion equation on graphs/networks” with @eestradalab is now out! You can find it open-access in Chaos, Solitons and Fractals. https://t.co/ERhYiek4jS

Network data release! A few years ago we (@NAChristakis @Bruce_Landon et al) used a social network approach to study polio vaccine hesitancy in India. Network, vaccine status, and baseline covariates here: https://t.co/O1rN2LSvYR. Original paper here: https://t.co/g7T5SvOxMT https://t.co/0dIVhefAYh

New #PhD position in the lab of Barbara Drossel. Very exciting place, with lots of things going on. If you fancy work on #dynamics, #ecology, #networks, and #complexity this is highly recommended.

https://jobs.pro-physik.de/Job/PhD-Positions-in-the-Theory-of-Complex-Systems-m-f-d.752355515.html?jssi=35596483380562434&jsix=0

Mediterranean School of Complex Networks
Salina, Sicily 25 June - 02 July 2022

https://mediterraneanschoolcomplex.net/

Our Mission

1. Provide a theoretical background to students (Master, PhD) and young researchers in the field, with particular attention to current trends in Network Science.

2. Promote philosophical and scientific exchange between all participants, i.e., lecturers and attendants.

دوره آنلاین مقدماتی یادگیری ماشین
- محمد ایران‌دوست


#علم_داده #یادگیری_ماشین #برنامه_نویسی

🔗 @SciSchool

"Mathematics of epidemic spreading" My talk at the NorthWestBiomathsDataScienceSeminars overwieving recents theoretical results on epidemic spreading is now online!
https://t.co/wPoFiH8xYg

Two great job opportunities UniBremen in the area of Computational Social Science #CSS

TT Faculty position:
https://t.co/86VeHTKwNz

#Postdoc position, working with:
https://t.co/VPn4NXyRdW

I am teaching an 8-week course at Oxford on the fundamentals of topological data analysis (including persistent homology, cellular sheaves, & discrete Morse theory).

All Notes: https://t.co/gqZwXbwuOG

All Videos: https://t.co/qAQHr0JW4l

Course thread! (0/8) https://t.co/A820x6otnj

One of my favorite scientific figures is this one of the entropy levels of 100 world cities by the orientation of streets. The cities with most ordered streets: Chicago, Miami, & Minneapolis. Most disordered: Charlotte, Sao Paulo, Rome & Singapore. Paper: https://t.co/DTd5JiahmF https://t.co/OCdTHHfrbN

"A First Look at First-Passage Processes" (by S. Redner): https://t.co/v0JbWBRbtb

"These notes are based on the lectures that I gave (virtually) at the Bruneck Summer School in 2021 on first-passage processes and some applications of the basic theory."

“It is more productive to consider how bad things could get if we keep giving the virus opportunities to outwit us. Then we might do more to ensure that this does not happen.”
https://t.co/qI6Y5XrFdR

Interested in network medicine and bioinformatics? Here is your intro: https://t.co/pobeyTiR10 produced by our students.
Join the band at: https://t.co/gyNUa6dPbx
Thanks, Ayan Chatterjee, Samuel Westby, Ula Widocki,

MATHEMATICS OF
LARGE NETWORKS
SUMMER SCHOOL

A P P L I C A T I O N D E A D L I N E : F E B 1 , 2 0 2 2

Quantifying Relevance in Learning and Inference

Review paper by Matteo Marsili, Yasser Roudi

https://arxiv.org/pdf/2202.00339

Learning is a distinctive feature of intelligent behaviour. High-throughput experimental data and Big Data promise to open new windows on complex systems such as cells, the brain or our societies. Yet, the puzzling success of Artificial Intelligence and Machine Learning shows that we still have a poor conceptual understanding of learning. These applications push statistical inference into uncharted territories where data is high-dimensional and scarce, and prior information on "true" models is scant if not totally absent. Here we review recent progress on understanding learning, based on the notion of "relevance". The relevance, as we define it here, quantifies the amount of information that a dataset or the internal representation of a learning machine contains on the generative model of the data. This allows us to define maximally informative samples, on one hand, and optimal learning machines on the other. These are ideal limits of samples and of machines, that contain the maximal amount of information about the unknown generative process, at a given resolution (or level of compression). Both ideal limits exhibit critical features in the statistical sense: Maximally informative samples are characterised by a power-law frequency distribution (statistical criticality) and optimal learning machines by an anomalously large susceptibility. The trade-off between resolution (i.e. compression) and relevance distinguishes the regime of noisy representations from that of lossy compression. These are separated by a special point characterised by Zipf's law statistics. This identifies samples obeying Zipf's law as the most compressed loss-less representations that are optimal in the sense of maximal relevance. Criticality in optimal learning machines manifests in an exponential degeneracy of energy levels, that leads to unusual thermodynamic properties.

A review/perspective of how our statistical field theory of information dynamics has been applied to better understand a variety of empirical complex systems.

https://t.co/0EtLPsf7LI

Mathematics Behind Critical Phenomena:
https://youtu.be/Ei7EMfdpCDc

Kalle Kytölä
Department of Mathematics and Systems Analysis

One #PhD opening to study complementarity mechanisms in networks #tudelft, sponsored by #NWO_Science.

This line of work is at the intersection of Network Science, Machine Learning, and Science of Science. Details at https://t.co/fPzLT8MgGv

A postdoc Researcher position open on optimization and machine learning fields! For details: https://t.co/OftbgNH0Oj

We offer a #postdoc position on network dynamics and modeling! Spread the word! The salary is expected to increase dramatically soon :)

Rachith Aiyappa & @yy shows social network models with a weighted belief network within each individual can unify simple and contagion dynamics
Very cool
#NetSciX2022 @NetSciX2022 https://t.co/AmCbop5wea