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| date = 2013
| pages = 7639–7643
}}</ref> and identification,<ref name="speakerid2011" /> sampling on manifolds, anomaly detection,<ref name="Mishne" /><ref>{{Cite journal|last1=Shabat|first1=Gil|last2=Segev|first2=David|last3=Averbuch|first3=Amir|date=2018-01-07|title=Uncovering Unknown Unknowns in Financial Services Big Data by Unsupervised Methodologies: Present and Future trends|url=http://proceedings.mlr.press/v71/shabat18a.html|journal=KDD 2017 Workshop on Anomaly Detection in Finance|language=en|volume=71|pages=8–19}}</ref> image inpainting,<ref name="Gepshtein" /> revealing brain resting state networks organization <ref name="Margulies_et_al_2016">
| last1 = Margulies
| first1 = Daniel S.
| last2 = Ghosh
| first2 = Satrajit S.
| last3 = Goulas
| first3 = Alexandros
| last4 = Falkiewicz
| first4 = Marcel
| last5 = Huntenburg
| first5 = Julia M.
| last6 = Langs
| first6 = Georg
| last7 = Bezgin
| first7 = Gleb
| last8 = Eickhoff
| first8 = Simon B.
| last9 = Castellanos
| first9 = F. Xavier
| last10 = Petrides
| first10 = Michael
| last11 = Jefferies
| first11 = Elizabeth
| last12 = Smallwood
| first12 = Jonathan
| url = https://doi.org/10.1073/pnas.1608282113
| title = Situating the default-mode network along a principal gradient of macroscale cortical organization
| journal = Proceedings of the National Academy of Sciences
| pages = 12574-12579
| volume = 113
| issue = 44
| year = 2016
}}</ref> and so on.
Furthermore, the diffusion maps framework has been productively extended to [[complex networks]]<ref>{{cite journal
| last1 = De Domenico
| first1 = Manlio
| url = https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.118.168301
| title = Diffusion geometry unravels the emergence of functional clusters in collective phenomena
| journal = Physical review letters
| volume = 118
| issue = 16
| pages = 168301
| year = 2017
}}</ref>, revealing a functional organisation of networks which differs from the purely topological or structural one.
==See also==
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