In the realm of complex network analysis, comprehending temporal dynamics is crucial. Current research largely focuses on identifying influential nodes through established centrality methods, often evaluated on real or synthetic networks. However, the process of constructing a temporal graph based on node features remains unexplored. This article presents an innovative model that highlights the evaluation of dynamic networks by employing the aggregation of threshold-based snapshot selection, which relies on node features. The network's topology shifts with changing feature vector values, prompting a snapshot when the change aligns with a predefined threshold. This approach enhances comprehension of evolving societal structures and networks by minimizing computational overhead for large networks, thereby improving the practicality and scalability of the model.