A New Multi-graph Transformation Method for Frequent Approximate Subgraph Mining

Resumen

Frequent approximate subgraph (FAS) mining has been successfully applied in several science domains. This is because in many real applications, approximate approaches have achieved better results than exact approaches. However, there are real applications based on multi-graphs where traditional FAS miners cannot be applied because they were not designed to deal with this type of graph. In this paper, we propose a new method for transforming multi-graphs into simple graphs and vice versa without losing topological or semantic information that allows using traditional FAS mining algorithms and returning the mined patterns to the multi-graph space. Finally, we analyze the performance of our proposed method over synthetic multi-graph collections and additionally we show the usefulness of our proposal in image classification tasks where images are represented as multi-graphs.