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Cypher was originally intended to be used with the graph database Neo4j, but was opened up through the openCypher project in October 2015. [ 3 ] The language was designed with the power and capability of SQL (standard query language for the relational database model ) in mind, but Cypher was based on the components and needs of a database built ...
Neo4j is a graph database management system (GDBMS) developed by Neo4j Inc. The data elements Neo4j stores are nodes, edges connecting them, and attributes of nodes ...
Graph databases are a powerful tool for graph-like queries. For example, computing the shortest path between two nodes in the graph. Other graph-like queries can be performed over a graph database in a natural way (for example graph's diameter computations or community detection).
Current graph database products and projects often support a limited version of the model described here. For example, Apache Tinkerpop [13] forces each node and each edge to have a single label; Cypher allows nodes to have zero to many labels, but relationships only have a single label (called a reltype). Neo4j's database supports undocumented ...
The resulting graph is a property graph, which is the underlying graph model of graph databases such as Neo4j, JanusGraph and OrientDB where data is stored in the nodes and edges as key-value pairs. In effect, code property graphs can be stored in graph databases and queried using graph query languages.
Cypher is a query language for the Neo4j graph database; DMX is a query language for data mining models; Datalog is a query language for deductive databases; F-logic is a declarative object-oriented language for deductive databases and knowledge representation. FQL enables you to use a SQL-style interface to query the data exposed by the Graph API.
The popularization of knowledge graphs and their accompanying methods have led to the development of graph databases such as Neo4j [22] and GraphDB. [23] These graph databases allow users to easily store data as entities and their interrelationships, and facilitate operations such as data reasoning, node embedding, and ontology development on ...
In graph theory, a clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. Evidence suggests that in most real-world networks, and in particular social networks, nodes tend to create tightly knit groups characterised by a relatively high density of ties; this likelihood tends to be greater than the average probability of a tie randomly established ...