---
title: closeness_centrality
url: https://nexyron.tech/docs/cypher/procedures/algorithms/centrality/closeness-centrality
description: Score nodes by how close they are, on average, to all other reachable nodes.
---

# closeness_centrality

## Generic Description

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

Simple example:

```cypher
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.

```cypher
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