Large-Scale Graph Classification Based on Evolutionary Computation with MapReduce

Abstract

Discriminative subgraph mining from a large collection of graph objects is a crucial problem for graph classification. Several main memory-based approaches have been proposed to mine discriminative subgraphs, but they always lack scalability and are not suitable for large-scale graph databases. Based on the MapReduce model, we propose an efficient method, MRGAGC, to process discriminative subgraph mining. MRGAGC employs the iterative MapReduce framework to mine discriminative subgraphs. Each map step applies the evolutionary computation and three evolutionary strategies to generate a set of locally optimal discriminative subgraphs, and the reduce step aggregates all the discriminative subgraphs and outputs the result. The iteration loop terminates until the stopping condition threshold is met. In the end, we employ subgraph coverage rules to build graph classifiers using the discriminative subgraphs mined by MRGAGC. Extensive experimental results on both real and synthetic datasets show that MRGAGC obviously outperforms the other approaches in terms of both classification accuracy and runtime efficiency.