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In many biomedical and social science studies, it is important to identify and predict the dynamic changes of associations among network data over time. We propose a varying-coefficient model to incorporate time-varying network data, and impose a piecewise penalty function to capture local features of the network associations. The proposed approach is semi-parametric, and therefore flexible in modeling dynamic changes of association in network data problems. Furthermore, the approach can identify the time regions when dynamic changes of associations occur. To achieve a sparse network estimation at local time intervals, we implement a group penalization strategy involving parameters that overlap between groups. However, this makes the optimization process challenging for large-dimensional network data observed at many time points. We develop a fast algorithm, based on the smoothing proximal-gradient method, that is computationally efficient and accurate. We illustrate the proposed method through simulation studies and children's attention deficit hyperactivity disorder fMRI data, showing that the proposed method and algorithm recover dynamic network changes over time efficiently.