Nodes, relationships, properties — first-class relationships versus RDF reification and junction tables

A property graph models data as nodes plus directed, typed relationships that are first-class records carrying their own properties, which is why 'Alice KNOWS Bob since 2020' is one edge here but a junction-table-plus-attributes mess in relational and a multi-triple reification in RDF.

Previously

Scene 1's pointer walk beat the join, but we cheated — we never said how a relationship gets to be a thing you can follow. Here's the model that makes it real: relationships are first-class records with their own type and properties, not rows in a join table. Now the obvious question — what does 'first-class' buy you at the byte level that a join row doesn't?

Scene 02

Nodes, relationships, properties

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What to watch for

One fact: 'Alice KNOWS Bob since 2020.' Watch it drawn three ways, left to right. RELATIONAL needs a junction table for the link AND a separate attributes table just to hold the date. RDF has to reify — the edge becomes its own blank node with extra triples. The PROPERTY GRAPH stores it as a single typed edge, Alice—[:KNOWS]→Bob, with {since:2020} living right on the edge. Count the moving parts: the property graph has the fewest. That right pane is the model the rest of the course is built on.

One fact — 'Alice KNOWS Bob since 2020' — drawn three ways.
RELATIONAL (left): a USER table, a KNOWS junction table for the link, and a SEPARATE attributes table because a join row has nowhere to put the date.
RDF (middle): the edge has no internal structure, so to attach the date you must reify — turn the relationship into its own blank node (_:k1) and emit extra triples.
PROPERTY GRAPH (right): nodes plus one directed, typed relationship that is a first-class record carrying {since:2020} on the edge itself.
Property graph (node/relationship) = data modeled as nodes + first-class typed relationships, both of which carry properties.
Label & property = a label is the node's category (:User, :Movie); a property is a key/value entry in the map ({since:2020}) stored directly on a node OR an edge — the move the other two models can't make cleanly.

Implementation

Highlighted lines are the ones running in the diagram right now.

PropertyGraph.record_fact

one edge carries the relationship's own properties

1def record_fact(g, a, rel_type, b, props):
2    # nodes are first-class; so is the relationship
3    g.merge_node(a)
4    g.merge_node(b)
5    # the edge IS a record — properties live ON it
6    g.add_edge(a, b, type=rel_type, props=props)
7    # e.g. add_edge('Alice','Bob','KNOWS',{since:2020})

Relational.record_fact

a link needs a junction row + a separate attrs row

1def record_fact(db, a, rel, b, attrs):
2    db.upsert('USER', a); db.upsert('USER', b)
3    # the link: just the two endpoints
4    db.insert(rel + '(a,b)', (a.id, b.id))
5    # attributes have nowhere to go but a side table
6    db.insert(rel + '_ATTRS', (a.id, b.id, attrs))

RDF.record_fact

reify: the edge becomes a blank node + extra triples

1def record_fact(store, a, rel, b, attrs):
2    bnode = store.fresh_blank_node()
3    store.add(a, rel, bnode)        # alice :knows _:k1
4    store.add(bnode, ':object', b)  # _:k1 :object bob
5    for k, v in attrs.items():
6        store.add(bnode, k, v)      # _:k1 :since 2020

Sources

Where this sits in Build a graph database (Neo4j / Dgraph-style)

Scene 02 of 16, in the Why graphs act — Friends-of-friends melts a join; edges go first-class.. The property-graph model is four things — nodes, typed relationships, labels, and properties on both — and putting key/values directly ON the edge is the move that relational join-tables and RDF triples can't make cleanly.

Up next. We've made the edge a first-class object on paper. But 'first-class' has to mean something physical: when I'm standing on Alice and I want Bob, how does the engine GET to the edge without looking it up in an index? The answer is the single most important idea in the whole course — and it's why a hop costs the same whether the graph has ten nodes or ten billion.

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