Social networks are an example of complex systems consisting of nodes that can interact with each other and based on these activities the social relations are defined. The dynamics and evolution of social networks are very interesting but at the same time very challenging areas of research. In this paper the formation and growth of one of such structures extracted from data about human activities within online social networking system is investigated. Dynamics of both local and global characteristics are studied. Analysis of the dynamics of the network growth showed that it changes over time—from random process to power-law growth. The phase transition between those two is clearly visible. In general, node degree distribution can be described as the scale-free but it does not emerge straight from the beginning. Social networks are known to feature high clustering coefficient and friend-of-a-friend phenomenon. This research has revealed that in online social network, although the clustering coefficient grows over time, it is lower than expected. Also the friend-of-a-friend phenomenon is missing. On the other hand, the length of the shortest paths is small starting from the beginning of the network existence so the small-world phenomenon is present. The unique element of the presented study is that the data, from which the online social network was extracted, represents interactions between users from the beginning of the social networking site existence. The system, from which the data was obtained, enables users to interact using different communication channels and it gives additional opportunity to investigate multi-relational character of human relations.
online social network complex system dynamics network growth and evolution dynamics of relationships of different types local and global network characteristics