The role of edge weights in social networks: Modelling structure and dynamics

The structure of social networks can influence various dynamic processes of human interaction and communication, such as opinion formation and spreading of information or infectious diseases. To facilitate simulation studies of such processes, we have developed a weighted network model to mimic the structure of real social networks, in particular taking into account the recent observations on weight-topology correlations.¹ The model iterates on a fixed size network, reaching a steady state through processes of weighted local searches, global random attachment, and random deletion of nodes, and it has essentially two parameters which can be used to tune network properties. The generated networks display community structure, with strong internal links and weak links connecting the communities.2 Similarly to empirical observations, strong ties correlate with overlapping neighborhoods, and under edge removal, the network becomes fragmented faster when weak ties are removed first.¹ As an example of the effects that such structural properties have on dynamic processes, we present preliminary results from studies of social dynamics describing the competition of two non-excluding opinions in a society. Our results show that the weighted community structure slows down the dynamics as compared to randomized reference networks.