Sentence examples for querying graphs from inspiring English sources

An important part of GDB technology is querying graphs.

Note that this is in contrast to the definition of query languages in the field of in-formation science, where graphical query languages are formal languages for querying graphs for graph-theoretical properties and/or attributes of nodes and edges.

Neo4j (www.neo4j.com) is a Java-based database designed to store and query graphs.

The framework can also incorporate other techniques for querying big graphs, by making big graphs small, including but not limited to the following.

After traversing the querying graph, all the low-level queries are extracted.

For each intermediate query, we build a querying graph where each abstract entity is linked to all its corresponding concrete entities (Fig. 5).

First, we perform a depth first search of the querying graph, and for each edge linking two abstract entities in the querying graph (thick black lines in Fig. 5), we calculate all the ways of instantiating it by associations between concrete entities (dotted lines in Fig. 5) together with their properties.

Intuitively, a querying graph is an undirected graph which regroups all the entities (abstract and concrete) and the links (between concrete or abstract entities) necessary to compute all the alternative queries from an intermediate query.

The results of NetMatch are subgraphs of the original graph connected in the same way as the querying graph, and therefore they can be viewed as candidate network motifs as a result of their similar topological features [ 14].

A querying graph is valid (i.e. there is at least one alternative query corresponding to the intermediate query) if (1) it is connected, (2) for each abstract entity there exists a concrete entity and (3) each association of the intermediate query corresponds to at least one association between two concrete entities.

Querying big graphs is hard, and cleaning big graphs is even harder.