![]() When it comes to analyzing graphs, algorithms explore the paths and distance between the vertices, the importance of the vertices, and clustering of the vertices. Graph algorithms-operations specifically designed to analyze relationships and behaviors among data in graphs-make it possible to understand things that are difficult to see with other methods. For example, to determine importance algorithms will often look at incoming edges, importance of neighboring vertices, and other indicators. The power of graphs is in analytics, the insights they provide, and their ability to link disparate data sources. They allow users to perform “traversal queries” based on connections and apply graph algorithms to find patterns, paths, communities, influencers, single points of failure, and other relationships, which enable more efficient analysis at scale against massive amounts of data. Graphs and graph databases provide graph models to represent relationships in data. Government statistics agencies, pharmaceutical companies, and healthcare organizations have adopted RDF graphs widely. The RDF model provides a way to publish data in a standard format with well-defined semantics, enabling information exchange. Every vertex and edge is identified by a unique URI, or Unique Resource Identifier. In the RDF model a statement is represented by three elements: two vertices connected by an edge reflecting the subject, predicate and object of a sentence-this is known as an RDF triple. They can represent complex concepts in a domain, or provide rich semantics and inferencing on data. They are often used for linked data, data integration, and knowledge graphs. RDF graphs (RDF stands for Resource Description Framework) conform to a set of W3C (Worldwide Web Consortium) standards designed to represent statements and are best for representing complex metadata and master data. ![]() In this example, a set of colleagues and their relationships are represented as a property graph.īecause they are so versatile, property graphs are used in a broad range of industries and sectors, such as finance, manufacturing, public safety, retail, and many others. The vertices and edges can have attributes, called properties, with which they are associated. A property graph has vertices that can contain detailed information about a subject, and edges that denote the relationship between the vertices. Property graphs are used to model relationships among data, and they enable query and data analytics based on these relationships. Both types of graphs consist of a collection of points (vertices) and the connections between those points (edges). ![]() The property graph focuses on analytics and querying, while the RDF graph emphasizes data integration. There are two popular models of graph databases: property graphs and RDF graphs. ![]()
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