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Lee, Woo Seok
복잡계 이론물리 연구단
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Machine learning assisted network classification from symbolic time-series

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Title
Machine learning assisted network classification from symbolic time-series
Author(s)
Panday, Atish; Woo Seok Lee; Dutta, Subhasanket; Jalan, Sarika
Publication Date
2021-03
Journal
CHAOS, v.31, no.3
Publisher
AMER INST PHYSICS
Abstract
Machine learning techniques have been witnessing perpetual success in predicting and understanding behaviors of a diverse range of complex systems. By employing a deep learning method on limited time-series information of a handful of nodes from large-size complex systems, we label the underlying network structures assigned in different classes. We consider two popular models, namely, coupled Kuramoto oscillators and susceptible-infectious-susceptible to demonstrate our results. Importantly, we elucidate that even binary information of the time evolution behavior of a few coupled units (nodes) yields as accurate classification of the underlying network structure as achieved by the actual time-series data. The key of the entire process reckons on feeding the time-series information of the nodes when the system evolves in a partially synchronized state, i.e., neither completely incoherent nor completely synchronized. The two biggest advantages of our method over previous existing methods are its simplicity and the requirement of the time evolution of one largest degree node or a handful of the nodes to predict the classification of large-size networks with remarkable accuracy.
URI
https://pr.ibs.re.kr/handle/8788114/9897
DOI
10.1063/5.0046406
ISSN
1054-1500
Appears in Collections:
Center for Theoretical Physics of Complex Systems(복잡계 이론물리 연구단) > 1. Journal Papers (저널논문)
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