An efficient graph-mining method for complicated and noisy data with real-world applications

摘要

In this paper, we present a novel graph database-mining method called APGM (APproximate Graph Mining) to mine useful patterns from noisy graph database. In our method, we designed a general framework for modeling noisy distribution using a probability matrix and devised an efficient algorithm to identify approximate matched frequent subgraphs. We have used APGM to both synthetic data set and real-world data sets on protein structure pattern identification and structure classification. Our experimental study demonstrates the efficiency and efficacy of the proposed method.