Statistical analysis of random graphs

There are four main areas of research in this analysis: structural, resource, normative and dynamic. Graph and stochastic models, network evolution models, methods involving ontologies, structural and relational models, machine learning methods, graph visualization methods, etc. are used to solve various problems in this field. The paper gives a brief description of popular computer social networks, considers various approaches and models used in calculating network characteristics, and lists software applications that help automate the analysis process. Some possible directions for further research in this area are outlined, in particular a more substantial adaptation of methods for processing textual information to network content. In order to generalize the behavior of specific network participants to the whole network, it is necessary to create a unified theory of social networks, which could study methods of detection and description of network characteristics, regularities of distribution of these characteristics, creation of methods, allowing to determine the causes of participants’ interactions by the structure of social network.