On Flow Authority Discovery in Social Networks

A central characteristic of social networks is that it facilitates rapid dissemination of information between large groups of individuals. This paper will examine the problem of determination of information flow representatives, a small group of authoritative representatives to whom the dissemination of a piece of information leads to the maximum spread. Clearly, information flow is affected by a number of different structural factors such as the node degree, connectivity, intensity of information flow interaction and the global structural behavior of the underlying network. We will propose a stochastic information flow model, and use it to determine the authoritative representatives in the underlying social network. We will first design an accurate RankedReplace algorithm, and then use a Bayes probabilistic model in order to approximate the effectiveness of this algorithm with the use of a fast algorithm. We will examine the results on a number of real social network data sets, and show that the method is more effective than state-of-the-art methods.