closeness_centrality

Generic Description

Score nodes by how close they are, on average, to all other reachable nodes.

Simple example:

CALL nexyron.closeness_centrality()

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 closeness_centrality when score nodes by how close they are, on average, to all other reachable nodes 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.

Conceptual Explanation

closeness_centrality 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 betweenness because it measures access efficiency rather than brokerage

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 betweenness because it measures access efficiency rather than brokerage.

Advanced Example

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

CALL nexyron.closeness_centrality() YIELD node_id, centrality
RETURN node_id, centrality
LIMIT 10

Real Use Cases

• fast-reach actors in communication graphs
• identifying operationally central services
• choosing efficient starting points for outreach or search
• compare algorithm output with domain labels or time windows before operationalizing it
• feed graph-analytic scores into ranked reports, feature contracts, or investigation queues only after checking the modeling assumptions

Real Limitations And Tradeoffs

• sensitive to disconnected graphs and reachability assumptions
• algorithm output reflects the projected graph, not an abstract real-world network outside the data model
• nearby algorithms can answer different questions even when their output columns look similar