Neo4j-style index-free adjacency gives you O(1) per-hop neighbor lookup per node; cost is proportional to the subgraph actually visited. A k-way self-join on a relational edge table has to produce and filter intermediate tuples at each join step, and intermediate result size can blow up multiplicatively even if the final result is small. A k-way self-join on a relational edge table has to produce and filter intermediate tuples at each join step, and intermediate result size can blow up multiplicatively even if the final result is small. For deep traversals with selective endpoints, the relational plan's intermediate materialization is exactly the problem native graph engines were built to avoid.
You can just as easily see nodes and edges in a property graph as propositions about the world. The nice thing is that you can model relationships between entities as first class entities. nodes have the implicit property of being non-fungible.
Do you know of any relational database that returns a query result as normalized tables the way neo4j returns a sub-graph?
Honestly, I won't convince you, so I'll leave you to the pleasure of your pointer chasing, L1 cache misses, multiplicative blowups, and fixed / inflexibly structured taxonomies.
In practice if you index the right properties and have the right relationships in Neo4j then queries are very fast. And neo4j doesn't have taxonomies. You might be thinking of RDF. One of the best things about Neo4j compared to relational databases is the lack of a rigid schema.
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Neo4j-style index-free adjacency gives you O(1) per-hop neighbor lookup per node; cost is proportional to the subgraph actually visited. A k-way self-join on a relational edge table has to produce and filter intermediate tuples at each join step, and intermediate result size can blow up multiplicatively even if the final result is small. A k-way self-join on a relational edge table has to produce and filter intermediate tuples at each join step, and intermediate result size can blow up multiplicatively even if the final result is small. For deep traversals with selective endpoints, the relational plan's intermediate materialization is exactly the problem native graph engines were built to avoid.
You can just as easily see nodes and edges in a property graph as propositions about the world. The nice thing is that you can model relationships between entities as first class entities. nodes have the implicit property of being non-fungible.
Do you know of any relational database that returns a query result as normalized tables the way neo4j returns a sub-graph?
Honestly, I won't convince you, so I'll leave you to the pleasure of your pointer chasing, L1 cache misses, multiplicative blowups, and fixed / inflexibly structured taxonomies.
In practice if you index the right properties and have the right relationships in Neo4j then queries are very fast. And neo4j doesn't have taxonomies. You might be thinking of RDF. One of the best things about Neo4j compared to relational databases is the lack of a rigid schema.