Sampling informative patterns from large single networks

The set of all frequent patterns that are extracted from a single network can be huge. A technique recently proposed for obtaining a compact, informative and useful set of patterns is output sampling, where a small set of frequent patterns is randomly chosen. However, existing output sampling algorithms work only in the transactional setting, where the database consists of a collection of relatively small graphs. In this paper, first we extend the output sampling framework to the single network setting where the database is a large single graph, counting supports of patterns is more complicated, and frequent patterns might be sampled based on any arbitrary target distribution. Then, we propose sampling techniques that are based on more interesting/informative measures or those that are specific to large single networks, such as product of the pattern size with its support, network compressibility, and pattern density. Finally, we study the empirical behavior of our algorithm in a real-world case study.