Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Small worlds: the dynamics of networks between order and randomness
Small worlds: the dynamics of networks between order and randomness
Computer
Linked: How Everything Is Connected to Everything Else and What It Means
Linked: How Everything Is Connected to Everything Else and What It Means
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamical Processes on Complex Networks
Dynamical Processes on Complex Networks
Bursty event detection from collaborative tags
World Wide Web
A study of homophily on social media
World Wide Web
Social media: are they underpinned by social or interest-based interactions?
Proceedings of the Fourth Annual Workshop on Simplifying Complex Networks for Practitioners
Multidimensional Social Network in the Social Recommender System
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Research challenges and perspectives on Wisdom Web of Things (W2T)
The Journal of Supercomputing
Mining most frequently changing component in evolving graphs
World Wide Web
Extracting news blog hot topics based on the W2T Methodology
World Wide Web
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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.