Deep learning super-diffusion in multiplex networks
Abstract Complex network theory has shown success in understanding the emergent and collective behavior of complex systems Newman 2010 Networks: An Introduction (Oxford: Oxford University Press). Many real-world complex systems were recently discovered to be more accurately modeled as multiplex netw...
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Published in | Journal of physic, complexity Vol. 2; no. 3; pp. 35011 - 35019 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
IOP Publishing
01.09.2021
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Subjects | |
Online Access | Get full text |
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Summary: | Abstract
Complex network theory has shown success in understanding the emergent and collective behavior of complex systems Newman 2010
Networks: An Introduction
(Oxford: Oxford University Press). Many real-world complex systems were recently discovered to be more accurately modeled as multiplex networks Bianconi 2018
Multilayer Networks: Structure and Function
(Oxford: Oxford University Press); Boccaletti
et al
2014
Phys. Rep.
544
1–122; Lee
et al
2015
Eur. Phys. J. B
88
48; Kivelä
et al
2014
J. Complex Netw.
2
203–71; De Domenico
et al
2013
Phys. Rev. X
3
041022—in which each interaction type is mapped to its own network layer; e.g. multi-layer transportation networks, coupled social networks, metabolic and regulatory networks, etc. A salient physical phenomena emerging from multiplexity is super-diffusion: exhibited by an accelerated diffusion admitted by the multi-layer structure as compared to any single layer. Theoretically super-diffusion was only known to be predicted using the spectral gap of the full Laplacian of a multiplex network and its interacting layers. Here we turn to machine learning (ML) which has developed techniques to recognize, classify, and characterize complex sets of data. We show that modern ML architectures, such as fully connected and convolutional neural networks (CNN), can classify and predict the presence of super-diffusion in multiplex networks with 94.12% accuracy. Such predictions can be done
in situ
, without the need to determine spectral properties of a network. |
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Bibliography: | JPCOMPX-100139.R1 |
ISSN: | 2632-072X 2632-072X |
DOI: | 10.1088/2632-072X/abe6e9 |