Universality class of explosive percolation in Barabási-Albert networks

Islam, Muzahidul; Hassan, M. K.

doi:10.1038/s41598-019-44446-2

Cited by 2 publications

(1 citation statement)

References 52 publications

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“…With percolation theory omnipresent across a vast range of applications such as social networks, traffic networks, infectious disease networks, amino acid networks, and even understanding the spread of forest fires, computer viruses, etc. ( [24][25][26][27]), our hope is that our methods may be used to better understand network dynamics across a wide range of fields.…”

Section: Plos Computational Biologymentioning

confidence: 99%

Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures

et al. 2023

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In clinical neuroscience, epileptic seizures have been associated with the sudden emergence of coupled activity across the brain. The resulting functional networks—in which edges indicate strong enough coupling between brain regions—are consistent with the notion of percolation, which is a phenomenon in complex networks corresponding to the sudden emergence of a giant connected component. Traditionally, work has concentrated on noise-free percolation with a monotonic process of network growth, but real-world networks are more complex. We develop a class of random graph hidden Markov models (RG-HMMs) for characterizing percolation regimes in noisy, dynamically evolving networks in the presence of edge birth and edge death. This class is used to understand the type of phase transitions undergone in a seizure, and in particular, distinguishing between different percolation regimes in epileptic seizures. We develop a hypothesis testing framework for inferring putative percolation mechanisms. As a necessary precursor, we present an EM algorithm for estimating parameters from a sequence of noisy networks only observed at a longitudinal subsampling of time points. Our results suggest that different types of percolation can occur in human seizures. The type inferred may suggest tailored treatment strategies and provide new insights into the fundamental science of epilepsy.

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Section: Plos Computational Biologymentioning

confidence: 99%