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In this paper, we present a horizontal view of social influence, more specifically a quantitative study of the influence of neighbours on the probability of a particular node to join a group, on four popular Online Social Networks (OSNs), namely Orkut, YouTube, LiveJournal, and Flickr. Neighbours in OSNs have a mutually acknowledged relation, most often defined as friendship, and they are directly connected on a graph of a social network. Users in OSNs can also join groups of users. These groups represent common areas of interest.We present a simple social influence model to describe and explain the group joining process of users on OSNs. To this end, we extract the social influence from data sets of OSNs of a million sample nodes. One of our findings is that a set of neighbours in the OSN is about 100 times more powerful in influencing a user to join a group than the same number of strangers.
Hui et al. (Wed,) studied this question.