Výsledky vyhledávání - "Hayasaka, Satoru"

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Explosive percolation in thresholded networks

Autor: Hayasaka, Satoru

Publikováno v: Physica A: 451: 1-9 (2016)

Explosive percolation in a network is a phase transition where a large portion of nodes becomes connected with an addition of a small number of edges. Although extensively studied in random network models and reconstructed real networks, explosive pe

Externí odkaz: http://arxiv.org/abs/1504.00050

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The ubiquity of small-world networks

Autor: Telesford, Qawi K., Joyce, Karen E., Hayasaka, Satoru, Burdette, Jonathan H., Laurienti, Paul J.

Small-world networks by Watts and Strogatz are a class of networks that are highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. These characteristics result in networks with unique properties of re

Externí odkaz: http://arxiv.org/abs/1109.5454

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Assessing the consistency of community structure in complex networks

Autor: Steen, Matthew, Hayasaka, Satoru, Joyce, Karen, Laurienti, Paul

Publikováno v: Physical Review E 84, 016111 (2011)

In recent years, community structure has emerged as a key component of complex network analysis. As more data has been collected, researchers have begun investigating changing community structure across multiple networks. Several methods exist to ana

Externí odkaz: http://arxiv.org/abs/1106.0041

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Universal fractal scaling of self-organized networks

Autor: Laurienti, Paul J., Joyce, Karen E., Telesford, Qawi K., Burdette, Jonathan H., Hayasaka, Satoru

Publikováno v: Physica A 390: 3608-3613 (2011)

There is an abundance of literature on complex networks describing a variety of relationships among units in social, biological, and technological systems. Such networks, consisting of interconnected nodes, are often self-organized, naturally emergin

Externí odkaz: http://arxiv.org/abs/1011.1228

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Exponential Random Graph Modeling for Complex Brain Networks

Autor: Simpson, Sean L., Hayasaka, Satoru, Laurienti, Paul J.

Publikováno v: PLoS ONE 2011: 6(5), e20039 (doi:10.1371/journal.pone.0020039)

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the liter

Externí odkaz: http://arxiv.org/abs/1007.3230

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Degree distributions in mesoscopic and macroscopic functional brain networks

Autor: Hayasaka, Satoru, Laurienti, Paul J.

We investigated the degree distribution of brain networks extracted from functional magnetic resonance imaging of the human brain. In particular, the distributions are compared between macroscopic brain networks using region-based nodes and mesoscopi

Externí odkaz: http://arxiv.org/abs/0903.4168

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