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Abstract
The structural evolution of large-scale complex networks over time reveals critical insights into their dynamic behavior and underlying interaction patterns. This study proposes a multiscale topological framework to characterize dynamic interaction patterns in evolving networks constrained by temporal evolution. By integrating temporal motifs, persistence homology, and multiresolution community detection, we demonstrate how topological signatures can be used to trace stability, transitions, and hierarchical organization in dynamic networks. Experiments on synthetic and real-world datasets—including communication and biological networks—highlight the efficiency and scalability of our approach. Our findings establish the methodological foundation for temporal-aware topological analysis in dynamic complex systems
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