In recent years, research in area of social network analysis grows rapidly, mainly thanks to the prevalence of the large-scale social network systems. These social network systems can be characterized by complex network structures and extensive content. Social network analysis can be applied to static or dynamic types of problems. The current research is mainly focused on analysis of topological properties and dynamic properties corresponding to evolution of networks and communities. Contextual information can help in analyzing these properties. Social network data comes mainly from many on-line services. The huge quantity of data can be consisting of millions of vertices and edges. Given the scale, complexity and dynamics, the traditional methods of social network analysis cannot be easily used. It becomes crucial for researchers to understand the dynamics of these networks. It is important to predict the next evolution and analyze trends in the network. Other current directions of research are detection of communities and analysis of community evolution in large social networks, content (topic) evolution, information diffusion in social networks, etc.