Mining Brokers in Dynamic Social Networks

The theory of brokerage in sociology suggests if contacts between two parties are enabled through a third party, the latter occupies a strategic position of controlling information flows. Such individuals are called brokers and they play a key role in disseminating information. However, there is no systematic approach to identify brokers in online social networks. In this paper, we formally define the problem of detecting top-$k$ brokers given a social network and show that it is NP-hard. We develop a heuristic algorithm to find these brokers based on the weak tie theory. In order to handle the dynamic nature of online social networks, we design incremental algorithms: WeakTie-Local for unidirectional networks and WeakTie-Bi for bidirectional networks. We use two real world datasets, DBLP and Twitter, to evaluate the proposed methods. We also demonstrate how the detected brokers are useful in diffusing information across communities and propagating tweets to reach more distinct users.