This chapter defines communities within social networks as entities formed by nodes and their connected edges, emphasizing their importance for understanding network structures. It focuses on community detection, which enables the analysis of network topology from a mesoscopic perspective, aids in understanding network functions, uncovers potential network structures, and reveals hidden information. This chapter will cover the mathematical description of communities, evaluation indicators for community detection, and classical algorithms for detecting both disjoint and overlapping communities. It will also address the significance of community detection in dynamic networks, a current hotspot and challenge in social network research. Additionally, this chapter explores the concepts related to community evolution, including evolution events and relevant algorithms. Finally, it discusses the use of artificial and real datasets commonly employed to test the effectiveness of community detection algorithms.