Neighborhood-based uncertainty generation in social networks

Imprecision, incompleteness and dynamic exist in a wide range of network applications. It is difficult to decide the uncertainty relationship among nodes since traditional models are not meaningful in uncertain networks, and the inherent computational complexity of the problems with uncertainty is always intractable. In this paper, we study how to capture uncertainty in networks by transforming a series of snapshots of a network to an uncertain graph. A novel sampling scheme is also proposed which enables the development of efficient algorithms to measure uncertainty in networks. Considering the practical aspects of neighborhood relationship in real networks, a framework is introduced to transform an uncertain network into a deterministic weighted network where the weights on edges can be measured by Jaccard-like index. The comprehensive experimental evaluation results on real data demonstrate the effectiveness and efficiency of our algorithms.