Investigating the issue of community detection in social networks

In the current decade, social networks are developing so much that some of these networks now have more than tens of millions of members. Today, data types are used in these networks; Data of various types of audio, video and text, each of which requires separate exploration. The multiplicity of applications and their diversity based on historical events and social behaviors are also among the challenges faced by the developers of these networks. One of the most important features of these networks is the existence of communities in them. With the help of community extraction methods, common group behaviors can be categorized and the study of each part of the network can be done centrally. Finding communities on social media is very complicated in terms of computing. Because in addition to the uncertainty of the number of communities, the internal density of communities is not the same. There are different methods for identifying communities in different types of networks that the choice or creation of a method depends on the characteristics of the network and the purpose of studying the network. On the other hand, each method presented in community detection has special features that provide a specific classification of existing methods. By knowing the appropriate classification and characteristics of each category, we can better choose the appropriate algorithm. In this article, the concept of society and its detection in social networks and methods in this field will be examined.