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1.AbstractTemporal networks have become increasingly pervasive in many real-world applications, including the functional connectivity analysis of spatially separated regions of the brain. A major challenge in analysis of such networks is the identification of noise confounds, which introduce temporal ties that are non-essential, or links that are formed by chance due to local properties of the nodes. Several approaches have been suggested in the past for static networks or temporal networksdoi:10.1101/2021.04.20.440711 fatcat:noxsmxqkcvdljjrjicombkkuba