RAM: Randomized approximate graph mining

Abstract

We propose a definition for frequent approximate patterns in order to model important subgraphs in a graph database with incomplete or inaccurate information. By our definition, frequent approximate patterns possess three main properties: possible absence of exact match, maximal representation, and the Apriori Property. Since approximation increases the number of frequent patterns, we present a novel randomized algorithm (called RAM) using feature retrieval. A large number of real and synthetic data sets are used to demonstrate the effectiveness and efficiency of the frequent approximate graph pattern model and the RAM algorithm. © 2008 Springer-Verlag.