Probabilistic graph summarization

Abstract

We study group-summarization of probabilistic graphs that naturally arise in social networks, semistructured data, and other applications. Our proposed framework groups the nodes and edges of the graph based on a user selected set of node attributes. We present methods to compute useful graph aggregates without the need to create all of the possible graph-instances of the original probabilistic graph. Also, we present an algorithm for graph summarization based on pure relational (SQL) technology. We analyze our algorithm and practically evaluate its scalability using an extended Epinions dataset as well as synthetic datasets. The experimental results show that our algorithm produces compressed summary graphs in reasonable time. © 2013 Springer-Verlag Berlin Heidelberg.