Reliable Clustering on Uncertain Graphs

Many graphs in practical applications are not deterministic, but are probabilistic in nature because the existence of the edges is inferred with the use of a variety of statistical approaches. In this paper, we will examine the problem of clustering uncertain graphs. Uncertain graphs are best clustered with the use of a possible worlds model in which the most reliable clusters are discovered in the presence of uncertainty. Reliable clusters are those which are not likely to be disconnected in the context of different instantiations of the uncertain graph. In this paper we provide a generalized reliability measurement from two basic intuitions (purity and size balance) to overcome the challenges from standard reliability criterion, and develop a novel k-means algorithm to solve the uncertain clustering problem. We present experimental results which illustrate the effectiveness and efficiency of our model and approachs.