Entity-relationship diagram

As explained in Graph Neural Networks in Action

An entity-relationship diagram is a visual model showing entities, relationships, attributes, and constraints in a data or graph design.

An entity-relationship diagram (ER diagram) visually specifies a graph’s entities, relationships, and constraints. Use it to make graph structure explicit before parameterizing rules or experimenting in a GNN pipeline. section 8.2.2

Use an ER diagram to reason about:

• Which node and edge types a graph contains, from homogeneous structures to heterogeneous datasets with multiple types. section 8.2.2
• The rules and constraints that organize graph elements through a conceptual schema. section 8.2.2
• How a conceptual schema can guide an instance model or later system-specific graph design. section 8.2.2

At left is the relationship nomenclature for ER diagrams. At right is an example of a conceptual schema using an ER diagram.

Review relationship nomenclature alongside a conceptual-schema example to connect diagram symbols with graph structure. section 8.2.2

How Graph Neural Networks in Action covers it

How deep it goes

Defines the diagram and relates it to schemas, but defers detailed graph-data-model treatment to another reference.

Examples

Shows relationship nomenclature, a conceptual schema, and schemas containing one, two, or three node and edge types.

What you can do after

Build explicit graph structures and rules that support parameterized GNN experiments.

How it connects

Connects ER diagrams with graph datasets, schemas, instance models, ontologies, property graphs, RDF graphs, and heterogeneous graphs.

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Also explained in

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  Data-Oriented Programming: Reduce software complexityA data entity diagram is a visual model that represents data entities and distinguishes association and composition relationships between them.

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Related concepts

• data modelproduced by · A data model is a structured representation of data that defines its entities, attributes, relationships, and applicable constraints.
• language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• positional collectionhas part · A positional collection is an ordered collection whose elements are accessed by their positions.
• schemauses · A data schema is a formal specification of the structure, fields, types, relationships, and constraints of data.
• indexinghas part · Indexing is the organization or use of positions, keys, or lookup structures to identify and retrieve selected data or elements.

This explainer was generated by Manning's knowledge graph from the text of Graph Neural Networks in Action. The section links point to the parts of the book it draws on.