Graph-Based relational learning with a polynomial time projection algorithm

The paper presents a new projection operator, named AC- projection, which exhibits good complexity properties as opposed to the graph isomorphism operator typically used in graph mining. We study the size and structure of the search space and some practical properties of the projection operator. These properties give us a specialization algorithm using simple local operations. Then we prove experimentally that we can achieve an important performance gain without or with non-significant loss of discovered patterns quality.