Journal article icon

Journal article

Persistent homology of time-dependent functional networks constructed from coupled time series

Abstract:

We use topological data analysis to study “functional networks” that we construct from time-series data from both experimental and synthetic sources. We use persistent homology with a weight rank clique filtration to gain insights into these functional networks, and we use persistence landscapes to interpret our results. Our first example uses time-series output from networks of coupled Kuramoto oscillators. Our second example consists of biological data in the form of functional magnetic res...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


Files:
Publisher copy:
10.1063/1.4978997

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
More by this author
Institution:
University of Oxford
Oxford college:
St Cross College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
More from this funder
Funding agency for:
Stolz, B
Grant:
EP/G037280/1
More from this funder
Funding agency for:
Stolz, B
Grant:
EP/G037280/1
More from this funder
Funding agency for:
Stolz, B
Grant:
EP/G037280/1
More from this funder
Funding agency for:
Stolz, B
Grant:
EP/G037280/1
More from this funder
Funding agency for:
Harrington, H
Grant:
EP/K041096/1
Publisher:
AIP Publishing Publisher's website
Journal:
Chaos Journal website
Volume:
27
Issue:
4
Article number:
047410
Publication date:
2017-04-28
Acceptance date:
2017-01-04
DOI:
EISSN:
1089-7682
ISSN:
1054-1500
Source identifiers:
668038
Keywords:
Pubs id:
pubs:668038
UUID:
uuid:4b711003-f431-44be-b919-d95a02776c38
Local pid:
pubs:668038
Deposit date:
2017-01-04

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP