RING: An Integrated Method for Frequent Representative Subgraph Mining

We propose a novel representative based subgraph mining model. A series of standards and methods are proposed to select invariants. Patterns are mapped into invariant vectors in a multidimensional space. To find qualified patterns, only a subset of frequent patterns is generated as representatives, such that every frequent pattern is close to one of the representative patterns while representative patterns are distant from each other. We devise the RING algorithm, integrating the representative selection into the pattern mining process. Meanwhile, we use R-trees to assist this mining process. Last but not least, a large number of real and synthetic datasets are employed for the empirical study, which show the benefits of the representative model and the efficiency of the RING algorithm.