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Graph schema

A graph schema is a specification of permitted node types, relationship types, properties, and structural constraints in a graph.

How 2 Manning books explain it

Graph Neural Networks in Action

A graph schema is a specification of the permitted node types, edge types, properties, and structural rules of a graph.

How it is used
Specifies how a graph dataset is represented in a system and supplies rules for checking its integrity.
How deep it goes
Works through elements, constraints, conceptual versus system schemas, and iterative integrity testing without prescribing one database implementation.
Examples
Uses recruitment-network checks for candidate attributes, Knows edges, and unique IDs, alongside diagrams with increasing node and edge types.
What you can do after
Build it from scratch by defining graph elements and constraints, mapping them to a system, and testing graph instances.

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Graph Algorithms for Data Science

A graph schema is a specification of the permissible node categories, relationship types, properties, and structural constraints in a graph.

How it is used
Guides iterative graph modeling and communicates which labels, relationships, and properties support planned network queries.
How deep it goes
Explains LPG schema notation and practical omissions, but stops short of formal standards for direction, optionality, or uniqueness.
Examples
Uses Twitter users, tweets, hashtags, links, follows, retweets, and inferred amplification relationships.
What you can do after
Build it from scratch by combining modeling decisions into a self-describing schema and reserving inferred edges for later validation.

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Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.