![]() ![]() An example of labeled graphs is on Facebook where we could have nodes we find by terms like friend or co-worker and the relations like friend of or partner of. Labeled graphs are used when different vertices and relationships between them are defined and identified by labels. Weighted graphs, where relationships between nodes have sometime of numerical evaluation. For instance, relationships on Twitter where a user can follow certain profiles without them following him. ![]() Directed graphs, where nodes and relationships are not bidirectional by default. For instance, friendship, relationships in the social network, Facebook. Not directed graphs, where nodes and relationships are interchangeable, and the relationship can be interpreted in any sense. A graph is formed by vertices or denoted by circles, and edges shown by intersecting lines. Neo4j uses graphs to represent data and the relationships between them. A graph database manages a graph and its indexes. Indexes map properties to nodes or relationships. A journey navigates a graph, which identifies paths that order nodes. A label groups nodes into relationships and/or option. Nodes are organized in relationships, which also have properties. A graph stores data in nodes, which have properties. This type of database store graphs, and as we have said before, a graph is one of the most generic and flexible data structure. An example of this database manager is Neo4j. However, a graph databases is highly flexible in terms of a structure. The performance and scalability of a graph-oriented database are variable and highly complex to implement. Among the most used graph database implementations are Neo4j, HyperBase-DB, and InfoGrid. Obviously, graph databases are only useful when the information can be easily represented as a network. Being stored in this way, it is more efficient to navigate between relationships than in a relational model. Relationships can also have attributes and direct queries can be made to relationships rather than to the nodes. Graphs give importance not only to data, but also to the relationships between nodes. A graph is represented as a set of nodes, which can be entities interconnected by edge or relationships. We need to recognize that graph on the first place in order to understand how a graph database stores data. Now, it's time to have a look at graph databases. We have revised document and key-value databases so far. Hello, we know the main characteristics, advantages, and disadvantages of NoSQL systems. ![]()
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