arXiv subjects

Edit Bognár

Publications and source records attributed to Edit Bognár.

3 recordsLinked to original sources

arXiv2024

Parameter estimation of epidemic spread in two-layer random graphs by classical and machine learning methods

Our main goal in this paper is to quantitatively compare the performance of classical methods to XGBoost and convolutional neural networks in a parameter estimation problem for epidemic spread. As we use flexible two-layer random graphs as the underlying network, we can also study how much the structure of the graphs in the training set and the test set can differ while to get a reasonably good estimate. In addition, we also examine whether additional information (such as the average degree of infected vertices) can help improving the results, compared to the case when we only know the time series consisting of the number of susceptible and infected individuals. Our simulation results also show which methods are most accurate in the different phases of the epidemic.

cs.SI↗

arXiv2024

Estimating the parameters of epidemic spread on two-layer random graphs: a classical and a neural network approach

In this paper, we study the spread of a classical SIR process on a two-layer random network, where the first layer represents the households, while the second layer models the contacts outside the households by a random scale-free graph. We build a three-parameter graph, called polynomial model, where the new vertices are connected to the existing ones either uniformly, or preferentially, or by forming random triangles. We examine the effect of the graph's properties on the goodness of the estimation of the infection rate $τ$, which is the most important parameter, determining the reproduction rate of the epidemic. In the classical maximum likelihood approach, to estimate $τ$ one needs to approximate the number of SI edges between households, since the graph itself is supposed to be unobservable. Our simulation study reveals that the estimation is poorer at the beginning of the epidemic, for larger preferential attachment parameter of the graph, and for larger $τ$. We present two heuristic improvement algorithms and establish our method to be robust to changes in average clustering of the graph model. We also extend a graph neural network (GNN) approach for estimating contagion dynamics for our two-layered graphs. We find that dense networks offer better training datasets. Moreover, GNN perfomance is measured better using the $l_2$ loss function rather than cross-entropy.

q-bio.PE↗

arXiv2020

Virus spread and voter model on random graphs with multiple type nodes

When modelling epidemics or spread of information on online social networks, it is crucial to include not just the density of the connections through which infections can be transmitted, but also the variability of susceptibility. Different people have different chance to be infected by a disease (due to age or general health conditions), or, in case of opinions, ones are easier to be convinced by others, or stronger at sharing their opinions. The goal of this work is to examine the effect of multiple types of nodes on various random graphs such as Erdős--Rényi random graphs, preferential attachment random graphs and geometric random graphs. We used two models for the dynamics: SEIR model with vaccination and a version of voter model for exchanging opinions. In the first case, among others, various vaccination strategies are compared to each other, while in the second case we studied sevaral initial configurations to find the key positions where the most effective nodes should be placed to disseminate opinions.

physics.soc-ph↗