# Labeled-property graph
See Neo4j A labeled-property graph model is represented by a set of nodes, relationships, properties, and labels. Both nodes of data and their relationships are named and can store properties represented by key–value pairs. Nodes can be labelled to be grouped. The edges representing the relationships have two qualities: they always have a start node and an end node, and are directed;[12] making the graph a directed graph. Relationships can also have properties. This is useful in providing additional metadata and semantics to relationships of the nodes.[13] Direct storage of relationships allows a constant-time traversal.[14]
The addition of Properties makes these graph databases ideal for highly refined searches.
A graph database (GDB) is a database that uses graph structures for semantic queries with nodes, edges, and properties to represent and store data.[1] A key concept of the system is the graph (or edge or relationship). The graph relates the data items in the store to a collection of nodes and edges, the edges representing the relationships between the nodes. The relationships allow data in the store to be linked together directly and, in many cases, retrieved with one operation. Graph databases hold the relationships between data as a priority. Querying relationships is fast because they are perpetually stored in the database. Relationships can be intuitively visualized using graph databases, making them useful for heavily inter-connected data.[2]
# Graph types There are multiple types of graphs that can be categorized. Gartner suggests the five broad categories of graphs:[16] **Social graph:** this is about the connections between people; examples include Facebook, Twitter, and the idea of six degrees of separation **Intent graph:** this deals with reasoning and motivation. **Consumption graph:** also known as the "payment graph", the consumption graph is heavily used in the retail industry. E-commerce companies such as Amazon, eBay and Walmart use consumption graphs to track the consumption of individual customers. Interest graph: this maps a person's interests and is often complemented by a social graph. It has the potential to follow the previous revolution of web organization by mapping the web by interest rather than indexing webpages. **Mobile graph:** this is built from mobile data. Mobile data in the future may include data from the web, applications, digital wallets, GPS, and Internet of Things (IoT) devices.
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