Zobrazeno 1 - 10
of 284
pro vyhledávání: '"Allard, Antoine"'
The Onion Decomposition has recently been shown to provide principled models of complex graphs that better reproduce the sparse networks found in nature, but at the cost of complicated connection rules. We propose a k-edge swapping MCMC algorithm to
Externí odkaz:
http://arxiv.org/abs/2409.20493
Hyperbolic models can reproduce the heavy-tailed degree distribution, high clustering, and hierarchical structure of empirical networks. Current algorithms for finding the hyperbolic coordinates of networks, however, do not quantify uncertainty in th
Externí odkaz:
http://arxiv.org/abs/2406.10711
In recurrent networks of leaky integrate-and-fire (LIF) neurons, mean-field theory has proven successful in describing various statistical properties of neuronal activity at equilibrium, such as firing rate distributions. Mean-field theory has been a
Externí odkaz:
http://arxiv.org/abs/2311.05442
Random networks are a powerful tool in the analytical modeling of complex networks as they allow us to write approximate mathematical models for diverse properties and behaviors of networks. One notable shortcoming of these models is that they are of
Externí odkaz:
http://arxiv.org/abs/2307.03559
One of the pillars of the geometric approach to networks has been the development of model-based mapping tools that embed real networks in its latent geometry. In particular, the tool Mercator embeds networks into the hyperbolic plane. However, some
Externí odkaz:
http://arxiv.org/abs/2304.06580
Publikováno v:
Proc. Natl. Acad. Sci. U.S.A., 121, e2312202121 (2023)
Current epidemics in the biological and social domains are challenging the standard assumptions of mathematical contagion models. Chief among them are the complex patterns of transmission caused by heterogeneous group sizes and infection risk varying
Externí odkaz:
http://arxiv.org/abs/2302.13358
First principle network models are crucial to make sense of the intricate topology of real complex networks. While modeling efforts have been quite successful in undirected networks, generative models for networks with asymmetric interactions are sti
Externí odkaz:
http://arxiv.org/abs/2302.09055
Autor:
Boudreau, Mariah C., Allen, Andrea J., Roberts, Nicholas J., Allard, Antoine, Hébert-Dufresne, Laurent
Publikováno v:
Bull. of Math. Biol. 85(2023)118
Forecasting disease spread is a critical tool to help public health officials design and plan public health interventions.However, the expected future state of an epidemic is not necessarily well defined as disease spread is inherently stochastic, co
Externí odkaz:
http://arxiv.org/abs/2302.03210
We study the problem of clustering networks whose nodes have imputed or physical positions in a single dimension, for example prestige hierarchies or the similarity dimension of hyperbolic embeddings. Existing algorithms, such as the critical gap met
Externí odkaz:
http://arxiv.org/abs/2301.10403
Over the last decade, random hyperbolic graphs have proved successful in providing geometric explanations for many key properties of real-world networks, including strong clustering, high navigability, and heterogeneous degree distributions. These pr
Externí odkaz:
http://arxiv.org/abs/2209.09201