adamic_adar

Generic Description

Score pairs by rare shared neighbors more than common ones.

Simple example:

CALL nexyron.adamic_adar()

Similarity and embedding procedures compare nodes by neighborhood overlap, random-walk context, or learned vector position. They are useful for link prediction, recommendations, deduplication, and candidate generation.

Consumer-Level Explanation

Use adamic_adar when score pairs by rare shared neighbors more than common ones is the graph-analysis or operational question you actually need to answer. Keep the call explicit because its YIELD columns, row grain, and required runtime state determine how the rest of the Cypher pipeline can safely compose it. Structural similarity and embedding similarity are not the same: overlap metrics are explainable and local, while embeddings can capture broader context but need downstream validation.

Conceptual Explanation

adamic_adar should be read as one named procedure contract in the broader Nexyron Cypher surface. The procedure call is not only a syntax hook: it defines what runtime state, projection, registry entry, or graph algorithm is being asked to operate, and it determines which output columns downstream YIELD, WITH, and RETURN stages can safely use.

More Detailed Explanation

This differs from common-neighbors by giving more weight to informative shared neighbors

In practical queries, start by deciding the row grain you want after the call: one row per node, one row per path, one row per registry object, one row per artifact, or one row per summary. Then keep that grain explicit with YIELD and named projections. That is the difference between a useful planner-facing example and a vague call that downstream tooling cannot safely compose. For contract-driven procedures, the executable examples on these pages intentionally inspect procedure metadata unless the required named artifacts are created in the same example.

How It Differs From Nearby Algorithms

This differs from common-neighbors by giving more weight to informative shared neighbors.

Advanced Example

This example yields named columns from adamic_adar and returns a bounded result shape that downstream planner tooling can compose without guessing column names or row grain.

CALL nexyron.adamic_adar() YIELD source, target, score
RETURN source, target, score
LIMIT 10

Real Use Cases

Real Limitations And Tradeoffs