Scalable And Adaptive Graph Querying With Mapreduce

We address the problem of processing graph pattern matching queries over a massive set of data graphs in this letter. As the number of data graphs is growing rapidly, it is often hard to process such queries with serial algorithms in a timely manner. We propose a distributed graph querying algorithm, which employs feature-based comparison and a filter-and-verify scheme working on the Map Reduce framework. Moreover, we devise an efficient scheme that adaptively tunes a proper feature size at runtime by sampling data graphs. With various experiments, we show that the proposed method outperforms conventional algorithms in terms of scalability and efficiency.