Analysis and implementation of graph indexing for graph database using GraphGrep algorithm

Graph database is a database that uses a graph structure to represent and manage the data. Not all types of data are suit for graph database, one that fits is the molecular graph data type. It has characteristic labeled vertices and undirected edges. Graph database also have indexing method. For molecular data type study case, GraphGrep is the most approriate method because it assume each node in the graph database has a unique number (id-node) and label (label-node). So it is suitable for molecular data type. GraphGrep using a hash table (fingerprint) as an index, comparing the graph database fingerprint with graph query fingerpint to filter the database and use Ullman algorithm to perform subgraph matching. By implementing GrapGrep, we can filter database up to 100% filtering based on length-path we used and get the exact answer set. We also get the most efficient length-path based on the deepest depth in a graph query.