Glossary

1.Graph Technology

Graph Computing:Generalized graph computing, including graph databases, graph analysis, and graph learning; Narrowly defined graph computing only includes graph analysis. The generalized definition is used by default.

Graph Database:Focuses on operations such as adding, deleting, modifying, querying, and transaction processing for graph data, such as TuGraph DB.

Graph Analysis:Focuses on algorithmic analysis and pattern analysis of entire graph data, such as TuGraph Analytics.

Graph Learning:Refers to the same meaning as graph neural networks and graph machine learning, such as TuGraph Learn.

Graph Intelligence:Same as generalized graph computing, including graph databases, graph analysis, and graph learning. It is usually used for conceptual promotion and not for products.

2.Specific Concepts

Vertex:Also known as node or point. Avoid using the term “node” as it is often used to refer to the number of servers and can be easily confused. “Node” refers to the meaning of combination, intersection, and crossing; “node” can refer to a milestone on a timeline (key node) or a processing entity in a computer system (such as a server in a computer network).

Edge:Refers to the connection between two vertices.

Degree:Refers to the number of incoming or outgoing edges on a vertex. Note that in non-graph theory scenarios, “hops” are often referred to as “degrees”.

Hop:Refers to the number of vertices between two vertices on a path (excluding the starting point and including the endpoint). “K-Hop” in the algorithm performance test refers to this meaning.

Vertex Label:Refers to the type of a vertex, also known as a point label.

Edge Label:Refers to the type of an edge, also known as an edge label.