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Time-varying nodal measures with temporal community structure: A cautionary note to avoid misinterpretation.

ABSTRACT: In network neuroscience, temporal network models have gained popularity. In these models, network properties have been related to cognition and behavior. Here, we demonstrate that calculating nodal properties that are dependent on temporal community structure (such as the participation coefficient [PC]) in time-varying contexts can potentially lead to misleading results. Specifically, with regards to the participation coefficient, increases in integration can be inferred when the opposite is occurring. Further, we present a temporal extension to the PC measure (temporal PC) that circumnavigates this problem by jointly considering all community partitions assigned to a node through time. The proposed method allows us to track a node's integration through time while adjusting for the possible changes in the community structure of the overall network.

SUBMITTER: Thompson WH

PROVIDER: S-EPMC7268033 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Time-varying nodal measures with temporal community structure: A cautionary note to avoid misinterpretation.

Thompson William Hedley WH Kastrati Granit G Finc Karolina K Wright Jessey J Shine James M JM Poldrack Russell A RA

Human brain mapping 20200214 9

In network neuroscience, temporal network models have gained popularity. In these models, network properties have been related to cognition and behavior. Here, we demonstrate that calculating nodal properties that are dependent on temporal community structure (such as the participation coefficient [PC]) in time-varying contexts can potentially lead to misleading results. Specifically, with regards to the participation coefficient, increases in integration can be inferred when the opposite is occ ...[more]

PMID: 32058633

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