Journal of Pattern Recognition Research

Next-generation database systems dealing with biomedical data, web relationships, network directories and structured documents often model the data as graphs. With the rapid increase in the availability of biological, chemical graph datasets, there is a growing need for effective and efficient graph querying methods. Due to the noisy and incomplete characteristics of these datasets, approximate graph matching methods are also required. We propose a subgraph searching system called GrepVS for large graph databases that incorporates efficient graph searching algorithms together with new efficient data storage and filtering techniques. GrepVS runs exact and approximate graph matching outperforming the other main graph searching methods on synthetic and real graphs databases.