145 research outputs found
Cooperative behaviour in complex systems
In my PhD thesis I studied cooperative phenomena arise in complex systems
using the methods of statistical and computational physics. The aim of my work
was also to study the critical behaviour of interacting many-body systems
during their phase transitions and describe their universal features
analytically and by means of numerical calculations. In order to do so I
completed studies in four different subjects. My first investigated subject was
a study of non-equilibrium phase transitions in weighted scale-free networks.
The second problem I examined was the ferromagnetic random bond Potts model
with large values of q on evolving scale-free networks which problem is
equivalent to an optimal cooperation problem. The third examined problem was
related to the large-q sate random bond Potts model also and I examined the
critical density of clusters which touched a certain border of a perpendicular
strip like geometry and expected to hold analytical forms deduced from
conformal invariance. The last investigated problem was a study of the
non-equilibrium dynamical behaviour of the antiferromagnetic Ising model on
two-dimensional triangular lattice at zero temperature in the absence of
external field and at the Kosterlitz-Thouless phase transition point.Comment: PhD thesis, 155 pages, 42 figure
From calls to communities: a model for time varying social networks
Social interactions vary in time and appear to be driven by intrinsic
mechanisms, which in turn shape the emerging structure of the social network.
Large-scale empirical observations of social interaction structure have become
possible only recently, and modelling their dynamics is an actual challenge.
Here we propose a temporal network model which builds on the framework of
activity-driven time-varying networks with memory. The model also integrates
key mechanisms that drive the formation of social ties - social reinforcement,
focal closure and cyclic closure, which have been shown to give rise to
community structure and the global connectedness of the network. We compare the
proposed model with a real-world time-varying network of mobile phone
communication and show that they share several characteristics from
heterogeneous degrees and weights to rich community structure. Further, the
strong and weak ties that emerge from the model follow similar weight-topology
correlations as real-world social networks, including the role of weak ties.Comment: 10 pages, 5 figure
The Scaling of Human Contacts in Reaction-Diffusion Processes on Heterogeneous Metapopulation Networks
We present new empirical evidence, based on millions of interactions on
Twitter, confirming that human contacts scale with population sizes. We
integrate such observations into a reaction-diffusion metapopulation framework
providing an analytical expression for the global invasion threshold of a
contagion process. Remarkably, the scaling of human contacts is found to
facilitate the spreading dynamics. Our results show that the scaling properties
of human interactions can significantly affect dynamical processes mediated by
human contacts such as the spread of diseases, and ideas
Social inequalities that matter for contact patterns, vaccination, and the spread of epidemics
Individuals socio-demographic and economic characteristics crucially shape
the spread of an epidemic by largely determining the exposure level to the
virus and the severity of the disease for those who got infected. While the
complex interplay between individual characteristics and epidemic dynamics is
widely recognized, traditional mathematical models often overlook these
factors. In this study, we examine two important aspects of human behavior
relevant to epidemics: contact patterns and vaccination uptake. Using data
collected during the Covid-19 pandemic in Hungary, we first identify the
dimensions along which individuals exhibit the greatest variation in their
contact patterns and vaccination attitudes. We find that generally privileged
groups of the population have higher number of contact and a higher vaccination
uptake with respect to disadvantaged groups. Subsequently, we propose a
data-driven epidemiological model that incorporates these behavioral
differences. Finally, we apply our model to analyze the fourth wave of Covid-19
in Hungary, providing valuable insights into real-world scenarios. By bridging
the gap between individual characteristics and epidemic spread, our research
contributes to a more comprehensive understanding of disease dynamics and
informs effective public health strategies.Comment: 33 pages, 22 figure
Detecting periodic time scales in temporal networks
Temporal networks are commonly used to represent dynamical complex systems
like social networks, simultaneous firing of neurons, human mobility or public
transportation. Their dynamics may evolve on multiple time scales
characterising for instance periodic activity patterns or structural changes.
The detection of these time scales can be challenging from the direct
observation of simple dynamical network properties like the activity of nodes
or the density of links. Here we propose two new methods, which rely on already
established static representations of temporal networks, namely supra-adjacency
matrices and temporal event graphs. We define dissimilarity metrics extracted
from these representations and compute their Fourier Transform to effectively
identify dominant periodic time scales characterising the original temporal
network. We demonstrate our methods using synthetic and real-world data sets
describing various kinds of temporal networks. We find that while in all cases
the two methods outperform the reference measures, the supra-adjacency based
method identifies more easily periodic changes in network density, while the
temporal event graph based method is better suited to detect periodic changes
in the group structure of the network. Our methodology may provide insights
into different phenomena occurring at multiple time-scales in systems
represented by temporal networks.Comment: 19 pages, 11 figure
Interpreting wealth distribution via poverty map inference using multimodal data
Poverty maps are essential tools for governments and NGOs to track
socioeconomic changes and adequately allocate infrastructure and services in
places in need. Sensor and online crowd-sourced data combined with machine
learning methods have provided a recent breakthrough in poverty map inference.
However, these methods do not capture local wealth fluctuations, and are not
optimized to produce accountable results that guarantee accurate predictions to
all sub-populations. Here, we propose a pipeline of machine learning models to
infer the mean and standard deviation of wealth across multiple geographically
clustered populated places, and illustrate their performance in Sierra Leone
and Uganda. These models leverage seven independent and freely available
feature sources based on satellite images, and metadata collected via online
crowd-sourcing and social media. Our models show that combined metadata
features are the best predictors of wealth in rural areas, outperforming
image-based models, which are the best for predicting the highest wealth
quintiles. Our results recover the local mean and variation of wealth, and
correctly capture the positive yet non-monotonous correlation between them. We
further demonstrate the capabilities and limitations of model transfer across
countries and the effects of data recency and other biases. Our methodology
provides open tools to build towards more transparent and interpretable models
to help governments and NGOs to make informed decisions based on data
availability, urbanization level, and poverty thresholds.Comment: 12 pages. In Proceedings of the ACM Web Conference 2023 (WWW'23
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