Towards a subgraph/supergraph cached query-graph index

Many modern big data applications deal with graph structured data, such as databases of molecular compounds represented as graphs of atoms and bonds, or “structured interaction networks” in biological and social networks, where nodes refer to entities (proteins, people, etc.) and edges represent their relationships. Central to high performance graph analytics over such data, is to locate patterns in dataset graphs. Informally, given a graph dataset and a query (a.k.a. pattern) graph g, the goal is to return stored graphs that contain g (subgraph querying) or are contained in g (supergraph querying). These operations are costly, as they entail the NPComplete subgraph isomorphism problem[1]. This is further aggravated when the dataset consists of a large number of graphs, as testing g for subgraph isomorphism against all of them would require a very large amount of time.