Maximum common subgraph mining: A fast and effective approach towards feature generation
Leander Schietgat, Fabrizio Costa, Jan Ramon, Luc De Raedt · Lirias · 2009
There exists a wide variety of local graph mining approaches that search for frequent, correlated or closed patterns in graphs. These methods typically return very large sets of patterns which can then be used as features to build classifiers. Here we take a different approach: rather than mining for all local patterns, we randomly sample from the set of maximum common subgraphs. The advantages are that maximum common subgraphs are easier to compute than frequent or correlated patterns, and that the resulting features lead to classification models that achieve significantly better predictive performance than models built on the patterns returned by traditional mining approaches.