Finally, we present two use cases that can highly benefit from the knowledge on k-bridges detected through our approach.Įxpansion of online social networks is rapid and furious. We also propose two further specializations of k-bridges, regarding Reddit and the network of patent inventors, to prove that the knowledge on k-bridges we initially found in Yelp is not limited to this social network. In addition, we investigate the relationship between k-bridges and the main centrality measures in the macro-categories of Yelp. We also analyze the relationship between k-bridges and power users. Then, we verify if there exists an influence exerted by k-bridges on their friends and/or on their co-reviewers. In particular, we investigate how some basic characteristics of Yelp k-bridges vary against k (i.e., against the number of macro-categories which the businesses reviewed by them belong to). Then, we analyze the specialization of this concept and algorithm in Yelp and we extract several knowledge patterns about Yelp k-bridges.

In this paper, we introduce the concept of k-bridge (i.e., a user who connects k sub-networks of the same network or k networks of a multi-network scenario) and propose an algorithm for extracting k-bridges from a social network.

Yelp define