Algorithms Using Local Graph Features to Predict Epidemics
Yeganeh Alimohammadi (Stanford)

People's interaction networks play a critical role in epidemics. However, precise mapping of the network structure is often expensive or even impossible. I will show that it is unnecessary to map the entire network. Instead, contact tracing a few samples from the population is enough to estimate an outbreak's likelihood and size.

More precisely, I start by studying a simple epidemic model where one node is initially infected, and an infected node transmits the disease to its neighbors independently with probability p. In this model, I will present a nonparametric estimator on the likelihood of an outbreak based on local graph features and give theoretical guarantees on the estimator's accuracy for a large class of networks. Finally, I will extend the result to the general SIR model with random recovery time: Local graph features are enough to predict the time evolution of epidemics on a large class of networks.

https://youtu.be/pOrEESEluxU

Mathematics of the COVID-19 Pandemics: Lessons Learned and How to Mitigate the Next One
Abba Gumel (University of Maryland)

The novel coronavirus that emerged in December 2019, COVID-19, is the greatest public health challenge humans have faced since the 1918 influenza pandemic (it has so far caused over 615 million confirmed cases and 6.5 million deaths). In this talk, I will present some mathematical models for assessing the population-level impact of the various intervention strategies (pharmaceutical and non-pharmaceutical) being used to control and mitigate the burden of the pandemic. Continued human interference with the natural ecosystems, such as through anthropogenic climate change, environmental degradation, and land use changes, make us increasingly vulnerable to the emergence, re-emergence and resurgence of infectious diseases (particularly respiratory pathogens with pandemic potential). I will discuss some of the lessons learned from our COVID-19 modeling studies and propose ways to mitigate the next respiratory pandemic.

https://youtu.be/KakzvrQf_CA

Complex Contagions and Hybrid Phase Transitions
Joel Miller (La Trobe University)

A complex contagion is an infectious process in which individuals may require multiple transmissions before changing state. These are used to model behaviours if an individual only adopts a particular behaviour after perceiving a consensus among others. We may think of individuals as beginning inactive and becoming active once they are contacted by a sufficient number of active partners. Here we study the dynamics of the Watts threshold model (WTM). We adapt techniques developed for infectious disease modelling to develop an analyse analytic models for the dynamics of the WTM in configuration model networks and a class of random clustered (triangle-based) networks. We derive conditions under which cascades happen with an arbitrarily small initial proportion active. We also observe hybrid phase transitions when cascades are not possible for small initial conditions, but occur for large enough initial conditions.

https://youtu.be/17QkYHxDaAY

Testing, Voluntary Social Distancing, and the Spread of an Infection
Ali Makhdoumi (Duke University)

In this talk, we present a modeling framework to study the effects of testing policy on voluntary social distancing and the spread of an infection. Agents decide their social activity level, which determines the social network over which the virus spreads. Testing enables the isolation of infected individuals, slowing down the infection. But greater testing also reduces voluntary social distancing or increases social activity, exacerbating the spread of the virus. We show that the effect of testing on infections is non-monotone. This non-monotonicity also implies that the optimal testing policy may leave some of the testing capacity of society unused. This also implies that testing should be combined with mandatory social distancing measures to avoid these adverse behavioral effects.

https://youtu.be/YEOnmfold74

Looking for a #PhD on comp. soc. science and/or net science? Get in touch! Topics include (many aspects of) cryptos/nfts/blockchain, social nets, misinformation, modelling of massive nets, etc. Lots of data, great team and close collab. with private and public organisations.

https://www.andreabaronchelli.com/

#PhD student in social data science

The student will join the Uppsala University Information Laboratory (https://uuinfolab.github.io). This project studies the role of images in the spread and anchoring of political ideas, and ultimately how visual politics contributes to persuasion and shaping the political agenda. Visual politics is increasingly prominent in digital public spaces, where image-based (dis)information spreads effectively. We know that images are a powerful form of political communication, that visual communication is perceived as convincing and affects people's perceptions of a variety of issues. At the same time, we still know surprisingly little about the actors who use images to promote political ideas, what mechanisms are at play, and even less what impact these activities have on public discourse. Your role in the project will be to develop and apply social data science research methods and methodologies that are suitable for analysing large-scale and complex visual spreading phenomena.

https://www.uu.se/en/about-uu/join-us/details/?positionId=561864

MSc Programme in "Social Data Science" at University College Dublin is now open for application.
The 1-year programme (2-years part-time) provides sociological thinking combined with data science skills.
https://lnkd.in/eVKmsGGh

Fully-funded #PhD scholarships are available with our CRT commencing in Sept 2023. Join 113 students & begin your 4-year PhD journey with us.
https://data-science.ie/how-do-i-apply/

Workshop: Machine Learning Glassy Dynamics

Amorphous materials such as glasses have been used since prehistoric times, but in recent years their technological applications have rapidly grown. However, despite many decades of research using experiments, theory and computer simulations, fundamental mechanisms such as mechanical properties and stress relaxation in glasses are still poorly understood. In recent years, machine learning techniques have started to be applied to help solve these fundamental questions, creating a new research path to study disordered materials. This workshop is dedicated to understanding the role that machine learning can play in better understanding glasses from a fundamental perspective. The original format should trigger new collaboration work between different actors and help organise this rapidly growing research field.

https://www.cnrs.fr/en/workshop-machine-learning-glassy-dynamics

🎞 https://www.youtube.com/playlist?list=PLLusiyVUCDJnprBuih5yDgToIzpsIdCi4

A journey from Network Medicine to understanding how food affects our health and diseases or the #Foodome project
A. Barabsi

https://youtu.be/FNfrp4giFlI

Open #PhD and #postdoc position in my soon-to-be newly established quantum information research group at Lund University, Sweden.

#job duration: 5 years for PhD and 2 years for postdoc. Starting in the first half of 2023.

Expertise is welcomed in any of the following topics: quantum correlations, Bell inequalities, entanglement, quantum communication, quantum key distribution, convex optimization, programming, quantum optics and open quantum systems.

PhD: https://lnkd.in/eRYSdF3h
Postdoc: https://lnkd.in/e3r5WGcr

Message passing methods on complex networks
Review article by M. E. J. Newman

Networks and network computations have become a primary mathematical tool for analyzing the structure of many kinds of complex systems, ranging from the Internet and transportation networks to biochemical interactions and social networks. A common task in network analysis is the calculation of quantities that reside on the nodes of a network, such as centrality measures, probabilities, or model states. In this review article we discuss message passing methods, a family of techniques for performing such calculations, based on the propagation of information between the nodes of a network. We introduce the message passing approach with a series of examples, give some illustrative applications and results, and discuss the deep connections between message passing and phase transitions in networks. We also point out some limitations of the message passing approach and describe some recently-introduced methods that address these limitations.

https://arxiv.org/abs/2211.05054

“Python for scientific computing” an on-line course aimed to improve your scientific Python skills starting Tuesday 22nd November at 10:00 EET (4 days, 3 hours per day). 1 ECTS available if you need it.

More info and registration at:
https://scicomp.aalto.fi/training/scip/python-for-scicomp-2022/

This course isn’t designed to teach Python itself, but if you know some Python, you will get a good introduction to the broader Python for science ecosystem and be able to use the right tools for your work and keep your code in good shape.

🌊Do you want to spend your next summer at the Baltic sea?
📈Are you studying data science or population science?
Apply for our Summer Research Visit in the Lab of Digital and Computational Demography at our Institute!

https://www.demogr.mpg.de/en/career_6122/jobs_fellowships_1910/population_and_social_data_science_summer_incubator_program_11474

Landau theory for the Mpemba effect through phase transitions
Roi Holtzman & Oren Raz

The Mpemba effect describes the situation in which a hot system cools faster than an identical copy that is initiated at a colder temperature. In many of the experimental observations of the effect, e.g. in water and clathrate hydrates, it is defined by the phase transition timing. However, none of the theoretical investigations so far considered the timing of the phase transition, and most of the abstract models used to explore the Mpemba effect do not have a phase transition. We use the phenomenological Landau theory for phase transitions to identify the second order phase transition time, and demonstrate with a concrete example that a Mpemba effect can exist in such models.

https://www.nature.com/articles/s42005-022-01063-2

#Job

https://www.jobbnorge.no/en/available-jobs/job/234196/associate-professor-in-computational-porous-media-physics

We are looking for a #postdoc researcher to work at the interface between Network Geometry and Machine Learning (dimension reduction techniques and neural networks).

http://bit.ly/3TPc9Xk

#PhD in Network Science

Developing methods to deal with uncertain time-evolving network data in order to enable more effective study of complex systems within and around us from our cellular biology to our social interactions

https://www.academictransfer.com/en/320231/phd-in-network-science/

Predicting a whole World Cup is hard. Predicting one match should be easier.

So how about England v Iran? What does Joshua Bull's model say?


https://youtu.be/KjISuZ5o06Q