pagerank

Generic Description

Score nodes by recursive importance based on incoming structure.

Simple example:

CALL nexyron.pagerank()

Centrality procedures rank nodes by structural importance. They are useful for influence, dependency, bottleneck, and exposure analysis, but each metric encodes a different meaning of importance.

Consumer-Level Explanation

Use pagerank when score nodes by recursive importance based on incoming structure 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. Do not treat centrality scores as interchangeable: degree measures direct volume, PageRank and eigenvector-style scores reward endorsement by important neighbors, betweenness finds bridge nodes, and closeness/harmonic scores emphasize reachability.

Conceptual Explanation

pagerank 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

PageRank differs from simple degree-like measures because it values endorsements from already-important neighbors more than endorsements from unimportant ones

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

PageRank differs from simple degree-like measures because it values endorsements from already-important neighbors more than endorsements from unimportant ones.

Advanced Example

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

CALL nexyron.pagerank() YIELD node_id, score
RETURN node_id, score
LIMIT 10

Real Use Cases

Real Limitations And Tradeoffs