Community detection in networks is a popular topic in modern network science. Communities, or clusters, are groups of vertices that are more likely to connect to each other than to members of other groups. Identifying communities is an ill-defined problem due to the lack of a universal definition and clear protocols for validation and performance comparison. This article provides a comprehensive guide to the main aspects of community detection, including the evolution from classic to modern definitions, validation methods, and an overview of popular clustering algorithms. It also discusses the strengths and weaknesses of various methods and offers guidance on their usage. The content is organized into sections covering the concept of communities, validation techniques, popular clustering approaches, software resources, and future outlook. The article aims to be accessible to practitioners with basic knowledge of network science, providing critical analysis and practical advice on community detection.Community detection in networks is a popular topic in modern network science. Communities, or clusters, are groups of vertices that are more likely to connect to each other than to members of other groups. Identifying communities is an ill-defined problem due to the lack of a universal definition and clear protocols for validation and performance comparison. This article provides a comprehensive guide to the main aspects of community detection, including the evolution from classic to modern definitions, validation methods, and an overview of popular clustering algorithms. It also discusses the strengths and weaknesses of various methods and offers guidance on their usage. The content is organized into sections covering the concept of communities, validation techniques, popular clustering approaches, software resources, and future outlook. The article aims to be accessible to practitioners with basic knowledge of network science, providing critical analysis and practical advice on community detection.