Generating Uncertain Networks Based on Historical Network Snapshots

Abstract

Imprecision, incompleteness and dynamic exist in wide range of network applications. It is difficult to decide the uncertainty relationship among nodes since traditional models do not make sense on uncertain networks, and the inherent computational complexity of problems with uncertainty is always intractable. In this paper, we study how to capture the uncertainty in networks by modeling a series snapshots of networks to an uncertain graph. Since the large number of possible instantiations of an uncertain network, a novel sampling scheme is proposed which enables the development of efficient algorithm to measure in uncertain networks; considering the practical of neighborhood relationship in real networks, a framework is introduced to transform the uncertain networks into deterministic weight networks where the weights on edges can be measured as Jaccard-like index. The comprehensive experimental evaluation on real data demonstrates the effectiveness and efficiency of our algorithms.