The increasing significance of social networks has led to information propagation and community formation being an interesting domain in data science. The data gathered from big social networks exhibit different community structures. These communities attract various users who grow complex networks. The main goal is to identify the impacting nodes responsible for community data flow. The Twitter network edges are considered in the study, which plays a vital role in representing the activities and relationships developed among the community members. Different communities evolve when the network is analyzed using different community detection algorithms. The network statistics are used for analysis by calculating the weighted degree distribution of nodes in this study. The network is analyzed according to persistent clusters using community detection algorithms like the Spinglass, Walktrap, Fastgreedy, Leading Eigenvector, Multilevel, Edge Betweenness, and Label Propagation. It is found that these measures are very useful in community detection and observing the spread of information in social networks.