Tracing Network Evolution Using the PARAFAC2 Model

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dc.contributor.authorRoald, Marie
dc.contributor.authorBhinge, Suchita
dc.contributor.authorJia, Chunying
dc.contributor.authorCalhoun, Vince
dc.contributor.authorAdali, Tulay
dc.contributor.authorAcar, Evrim
dc.date.accessioned2020-01-27T17:34:49Z
dc.date.available2020-01-27T17:34:49Z
dc.date.issued2019-10-23
dc.description.abstractCharacterizing time-evolving networks is a challenging task, but it is crucial for understanding the dynamic behavior of complex systems such as the brain. For instance, how spatial networks of functional connectivity in the brain evolve during a task is not well-understood. A traditional approach in neuroimaging data analysis is to make simplifications through the assumption of static spatial networks. In this paper, without assuming static networks in time and/or space, we arrange the temporal data as a higher-order tensor and use a tensor factorization model called PARAFAC2 to capture underlying patterns (spatial networks) in time-evolving data and their evolution. Numerical experiments on simulated data demonstrate that PARAFAC2 can successfully reveal the underlying networks and their dynamics. We also show the promising performance of the model in terms of tracing the evolution of task-related functional connectivity in the brain through the analysis of functional magnetic resonance imaging data.en_US
dc.description.urihttps://arxiv.org/abs/1911.02926en_US
dc.format.extent6 pagesen_US
dc.genrejournal articles preprintsen_US
dc.identifierdoi:10.13016/m2r7vf-gn9l
dc.identifier.citationRoald, Marie; Bhinge, Suchita; Jia, Chunying; Calhoun, Vince; Adalı, Tulay; Acar, Evrim; Tracing Network Evolution Using the PARAFAC2 Model; Applications (2019); https://arxiv.org/abs/1911.02926en_US
dc.identifier.urihttp://hdl.handle.net/11603/17075
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectPARAFAC2en_US
dc.subjecttensor factorizationsen_US
dc.subjectnetwork evolutionen_US
dc.subjectdynamic networksen_US
dc.subjecttime-evolving dataen_US
dc.titleTracing Network Evolution Using the PARAFAC2 Modelen_US
dc.typeTexten_US

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