Month: March 2023

Postdoctoral research associate on Spreading phenomena on geometric networks

Employer: Rényi Institute of Mathematics

Place: Budapest, Hungary

Research theme: epidemic modeling, network science, graph theory, geometric networks, metapopulation models

Scientific directors: Dr. Márton Karsai (karsai.marton@gmail.com ) & Prof. Dr. László Lovász (laszlo.lovasz@ttk.elte.hu )

Network Epidemics Group @ Rényi Insitute

The Network Epidemics Group at the Rényi Institute works on the mathematical, computational, and data-driven modelling of dynamical epidemiological processes on graphs and networks. On one hand, the group plays special focus on the mathematical foundation of geometric network effects on evolving spreading processes, and on the other hand, on the data-driven simulations of epidemic processes to observe and understand real-world spreading phenomena. The group is led by Dr. Márton Karsai and Pr. László Lovász and functions as a member of the Health Security National Laboratory in Hungary.

Mission

It is a fundamental question in disease modeling how the structure and dynamics of social interactions and mobility mixing patterns influence an ongoing epidemic. These behavioral patterns can be effectively represented as networks, that provide effective tools for the mathematical and computational modelling of epidemic phenomena. They contribute to a better approximation that incorporates non-homogeneous mixing patterns within and between populations, which can build up into meta-population networks to describe how epidemics spread in countries or even around the globe.

The geometric structure and spatial organization of interaction and mobility networks play special roles in the emergence of a rich but largely unexplored set of spreading phenomena. One of these phenomena is the commonly observed spatial clustering of infection cases during the sub-sequent waves of the actual COVID-19 pandemic. While these phenomena can be related to the inhomogeneous spatial distribution of susceptible populations, local patterns of herd immunity or the different seeding scenarios of an actual wave, their emergence is substantially depending on the geometric nature of the underlying social and mobility networks.

In this project we aim to tackle this problem from two different directions:

Computational modelling of epidemic processes on geometric networks: to develop a spatially embedded meta-population framework, relying on data from Hungary, that is capable to reproduce rich class of spatially clustered patterns of infected cases in the country.
Mathematical modelling: to develop the mathematical foundation of these observed phenomena by identifying the fundamental graph properties of the underlying network structures that can induce the observed geometric patterns of infection clustering.

Read the full article at: renyi.hu

Fundamental limits to learning closed-form mathematical models from data

Oscar Fajardo-Fontiveros, Ignasi Reichardt, Harry R. De Los Ríos, Jordi Duch, Marta Sales-Pardo & Roger Guimerà 

Nature Communications volume 14, Article number: 1043

Given a finite and noisy dataset generated with a closed-form mathematical model, when is it possible to learn the true generating model from the data alone? This is the question we investigate here. We show that this model-learning problem displays a transition from a low-noise phase in which the true model can be learned, to a phase in which the observation noise is too high for the true model to be learned by any method. Both in the low-noise phase and in the high-noise phase, probabilistic model selection leads to optimal generalization to unseen data. This is in contrast to standard machine learning approaches, including artificial neural networks, which in this particular problem are limited, in the low-noise phase, by their ability to interpolate. In the transition region between the learnable and unlearnable phases, generalization is hard for all approaches including probabilistic model selection.

Read the full article at: www.nature.com

Universal patterns in egocentric communication networks

Gerardo Iñiguez, Sara Heydari, János Kertész, Jari Saramäki
Tie strengths in social networks are heterogeneous, with strong and weak ties playing different roles at both the network and the individual level. Egocentric networks, networks of relationships around a focal individual, exhibit a small number of strong ties and a larger number of weaker ties, a pattern that is evident in electronic communication records, such as mobile phone calls. Mobile phone data has also revealed persistent individual differences within this pattern. However, the generality and the driving mechanisms of this tie strength heterogeneity remain unclear. Here, we study tie strengths in egocentric networks across multiple datasets containing records of interactions between millions of people over time periods ranging from months to years. Our findings reveal a remarkable universality in the distribution of tie strengths and their individual-level variation across different modes of communication, even in channels that may not reflect offline social relationships. With the help of an analytically tractable model of egocentric network evolution, we show that the observed universality can be attributed to the competition between cumulative advantage and random choice, two general mechanisms of tie reinforcement whose balance determines the amount of heterogeneity in tie strengths. Our results provide new insights into the driving mechanisms of tie strength heterogeneity in social networks and have implications for the understanding of social network structure and individual behavior.

Read the full article at: arxiv.org

Scaling of the morphology of African cities

Rafael Prieto-Curiel, Jorge E. Patino, and Brilé Anderson

120 (9) e2214254120

The emptiness, elongation, and sprawl of a city have lasting implications for cities’ future energy needs. This paper creates a publicly available set of urban form indicators and estimates intercity distances. It uses footprint data of millions of buildings in Africa as well as the boundaries of urban agglomerations, street network data, and terrain metrics to detect different extension patterns in almost six thousand cities. These methods estimate the increasingly longer commutes in urban areas and the energy needed to move millions of people. Designing compact, dense, and better-connected urban forms will help cities be more sustainable and liveable.

Read the full article at: www.pnas.org