Dynamics of Joining Social Networks

Abstract

Online social networks constitute digital ecosystems. As social networks join with one another there is a need to study how the structure of joined network evolves over time. We have simulated the growth of joint networks that are formed by merger of network models using growth algorithms. We have proposed a new growth algorithm which allows us to emulate the internal evolution within the network while new nodes are added, which was missing in Barabasi's method of evolution. Though no method could completely emulate the growth of a real world social network, our proposed approach does not only include external or newly added actors but also actors that are preexistent in the network. We have seen from the degree distributions of the grown network that our new method of attachment has accentuated the scale free properties of the joined network. We also found limits to degree of highly connected nodes in accordance with Dunbar's number.