This paper presents a new algorithmic framework for transforming non-overlapping community detections into overlapping community structures in networks. Our approach begins with the transformation of the original graph \(G\) into a graph \(G'\) , preparing it for the application of any standard non-overlapping community detection algorithm. After applying a rapid detection of non-overlapping communities in graph \(G'\) , our method deduces overlapping communities in the original graph \(G\) by calculating the degrees of membership for nodes. This method not only captures the complex, multifaceted interactions within networks but also addresses the common question of how to use fast classical community detection algorithms, such as the Louvain method, to identify overlapping communities. Our results on both benchmark and real-world datasets demonstrate the efficiency of our approach in uncovering overlapping community structures.