A survey of clustering large probabilistic graphs: Techniques, evaluations, and applications

Abstract

AbstractGiven the growth of uncertainty in the real world, analysing probabilistic graphs is crucial. Clustering is one of the most fundamental methods of mining probabilistic graphs to discover the hidden patterns in them. This survey examines an extensive and organized analysis of the clustering techniques of large probabilistic graphs proposed in the literature. First, the definition of probabilistic graphs and modelling them are introduced. Second, the clustering of such graphs and their challenges, such as uncertainty of edges, high dimensions, and the impossibility of applying certain graph clustering techniques directly, are expressed. Then, a taxonomy of clustering approaches is discussed in two main categories: threshold‐based and possible worlds‐based methods. The techniques presented in each category are explained and examined. Here, these methods are evaluated on real datasets, and their performance is compared with each other. Finally, the survey is summarized by describing some of the applications of probabilistic graph clustering and future research directions.