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Community detection is a common problem in graph data analytics. It consists of finding groups of densely connected nodes with few connections to nodes outside of the group. In particular, identifying communities in large-scale networks is an important task in many scientific domains. Community detection algorithms are used to study the structural properties of real-world networks. In this review, we evaluated some of the traditional algorithms for overlapping and disjoint community detection on large-scale real-world networks to identify the best community in real-time networks.