DIVISION OF SOCIAL NETWORKS INTO TWO COMMUNITIES USING THE MAXIMUM LIKELIHOOD METHOD

Abstract

Identification of communities in graphs is the task of dividing graph nodes into groups (communities) based on the structure and connections between them. This is an important task in social network analysis, bioinformatics, physics and graph theory. There are many methods for extracting communities in graphs. Each method has its advantages and disadvantages and can be used depending on the specific task.

The development of new methods for extracting communities in graphs is an active area of research. For example, the maximum connectivity method and the tree-based clustering method have been developed recently and have shown high accuracy of community extraction on social network and bioinformatics data, respectively.

Comparative analysis of various methods for identifying communities in graphs can help you choose the most appropriate method for a particular problem. This analysis may include factors such as the accuracy of community extraction, runtime, and the ability to work with large datasets.

When social networks are divided into groups, it is important to find the most realistic situation in them. If a division into 2 groups is supposed, that is, if a group is divided into 2 in a social network, then the problem of predicting how these groups will look like is considered in the case of a dodecagonal network. The division into teams was calculated using the Maple program.