Graph modelling guidelines

Summary

Graph modelling uses graph theory to represent real-world entities, relationships, and properties through nodes and edges, unlocking insights across interconnected datasets. The process begins with defining domain concepts, mapping entities and relationships, and assigning relevant descriptive properties to both nodes and edges. Practitioners choose between graph structures such as property graphs or Resource Description Framework models before optionally developing a structural schema. Data is then imported or generated to populate storage systems such as Neo4j, Amazon Neptune, Azure Cosmos DB, or in-memory data structures. Finally, the graph is visualised and analysed using algorithmic methods like shortest path calculation, centrality measurement, and community detection.

Context

Traditional data modelling approaches can struggle to decipher relationships between entities and reveal insights within complex, interconnected datasets.

Approach / What changed

An eight-step graph modelling workflow that covers defining the domain, identifying entities and relationships, establishing properties, selecting a graph model, designing a schema, loading data, storing the graph, and performing analysis.

Takeaways

• Property graphs accommodate properties on both nodes and edges, whereas Resource Description Framework (RDF) relies on subject-predicate-object triples.
• Graph implementations can be stored in dedicated graph databases such as Neo4j, Amazon Neptune, or Microsoft Azure Cosmos DB, as well as non-graph databases or in-memory structures.
• Graph analysis utilizes algorithms such as shortest path, centrality, and community detection alongside visual representations to evaluate network structure.

Related reading

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  Unsupervised graph anomaly detection - Catching new fraudulent behaviours

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  Yonghao Hu

• Grab · Oct 25, 2022

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