We are pleased to announce that our paper
“Preferential Attachment in Online Networks: Measurement and Explanations” has been accepted for the ACM Web Science Conference (www.websci13.org).
The paper was a joined work together with Jerome Kunegis from the University of Koblenz, Germany and Christine Moser from UV University of Amsterdam, Netherlands.
In this paper we performed an empirical study of the preferential attachment phenomenom in temporal networks and show that on the Web, networks follow a nonlinear preferential attachment model in which the exponent depends on the type of network considered. The classical preferential attachment model for networks (Barabasi and Albert 1999) assumes a linear relationship between the number of neighbours of a node in network and the probability of attachment.
Although this assumption is widely made in Web Science and related fields, the underlying linearity is rarely measured. We performed an empirical longitudinal (time-based) study on forty-seven diverse Web network datasets from seven network categories. We show that contrary to the usual assumption, preferential attachment is nonlinear in the networks under consideration. We observe a dependency between the non linearity and the type of network under consideration – sublinear preferential attachment in certain types of networks, and superlinear attachment in others.
We propose explanations for the behaviour of that network measure, based on the mechanisms underlying the growth of the network in question.
You can access the paper here.