A social network is a social structure that is composed of various individual or organizational groups. The use of social networks has increased widely in recent years. One of the active and challenging research areas in the discussion of social networks is the issue of community detection. The general meaning of community in a social network is the gathering of a number of agents together in such a way that the members of each community have the most interactions with each other. Humans tend to form groups in these networks based on their similar interests. Such groups are known as communities or clusters. Detecting such a structure gives us an exceptional understanding of the organization and functioning of social networks. One of the most important features in these networks is the existence of community structure. In recent years, many community detection algorithms have been proposed to reveal the structural features and dynamic behaviors of networks. The main objective of this paper is to provide an overview of community detection algorithms and to examine the strengths and weaknesses of each community detection approach, ranging from traditional algorithms to advanced algorithms for overlapping community detection. Algorithms based on dimensionality reduction techniques such as non-negative matrix factorization (NMF) and principal component analysis (PCA) are also considered.