Short paths count more
A node one link away contributes 1. A node two links away contributes ½. Longer paths contribute less.
Harmonic centrality · domain calculator
Harmonic Centrality
Calculator
Look up a domain’s harmonic centrality score and rank in Common Crawl’s published web graph. Then explore what the metric measures, how it works, and how to interpret the result.
DOMAIN LEVEL · SCORE + GRAPH RANK · NO ACCOUNT
Calculator data is not loaded yet. This site looks up precomputed harmonic centrality from a Common Crawl release. Learn what data the calculator uses.
The metric
What harmonic centrality measures
In a link graph, domains are nodes and links are directed edges. Harmonic centrality measures how close a domain is to the other domains it can reach by following paths through that graph.
HC(v) = Σu ≠ v 1 / d(v, u)
Unreachable nodes add zero
Only nodes reachable from the domain through directed links contribute to its harmonic centrality.
Score and rank are different
The score is a raw sum. The rank orders domains within a particular graph release; lower rank numbers are higher.
Read the full definition · See the calculation steps · Understand score and rank
Learn the concept
Harmonic centrality questions
Start with the definition, then follow the links to formulas, graph direction, score interpretation, and Common Crawl coverage.
How does this harmonic centrality calculator work?
Enter a domain; the tool normalizes it and looks up its precomputed score and rank in the selected Common Crawl graph release.
READ ANSWER →What is harmonic centrality?
Harmonic centrality measures how close a node is to the other nodes it can reach in a network.
READ ANSWER →How is harmonic centrality calculated?
For each node, add 1 divided by the shortest path distance to every other reachable node.
READ ANSWER →What do the harmonic centrality score and rank mean?
The score is a raw sum of reachable-node contributions. The rank is its position among domains in that graph release; lower rank is better.
READ ANSWER →What does Common Crawl harmonic centrality measure?
Common Crawl publishes precomputed harmonic centrality for host-level and domain-level graphs built from links found in its crawls.
READ ANSWER →Is harmonic centrality a Google ranking factor?
No. This is a graph metric computed from Common Crawl data, not a Google score or a confirmed Google ranking factor.
READ ANSWER →Can harmonic centrality help with AI Overviews and AI search engines?
It can be an exploratory signal for connectedness and entity discovery, but it is not a confirmed Google, ChatGPT, or Claude ranking factor.
READ ANSWER →Data and limits
A Common Crawl graph measure
This calculator reads the imported domain-level score published with a Common Crawl graph release. The graph aggregates hosts into domains and reflects crawl coverage and the links represented in that release. It is a description of graph structure—not a search engine position, backlink count, or content-quality score.
What exactly does Common Crawl measure? → Is Harmonic Centrality a Google ranking factor? →
Data source: Common Crawl Web Graphs. See also its harmonic centrality methodology and graph statistics.
Go deeper
Harmonic centrality articles
Start with the formula, then see how a graph signal can support careful AI-search research.
Formula guide
The Harmonic Centrality Formula Explained With a Web-Graph Example
Harmonic centrality is a closeness measure for networks. This guide walks from the equation to a small web graph, then connects the calculation to the Common Crawl domain calculator.
READ ARTICLE →AI search research
Using Harmonic Centrality for AI Search and AI Overviews Research
AI search changes the questions SEO teams ask. Harmonic centrality can add a graph-structure lens, but it should be used as evidence for investigation—not as a promise of visibility in an AI answer.
READ ARTICLE →SEO measurement
Harmonic Centrality vs. Backlink Counts: Why They Differ
Backlink totals count links. Harmonic Centrality describes shortest-path reachability across a graph. They answer different questions and should be read together.
READ ARTICLE →AI data and evidence
What AI Training Data Has to Do With Harmonic Centrality
Common Crawl is used in important public web datasets and has been cited by AI developers. That creates a reasonable research hypothesis for Harmonic Centrality, but it does not prove a model uses the metric as a ranking or trust factor.
READ ARTICLE →Practical workflow
Can Harmonic Centrality Guide Internal Linking Research?
Harmonic Centrality can highlight graph position at domain level. Pair it with page-level crawling and human review before changing an internal-link strategy.
READ ARTICLE →Data interpretation
How to Compare Harmonic Centrality Across Common Crawl Releases
A changed score can reflect a changed web graph as well as a changed domain. This guide explains a careful release-to-release comparison workflow.
READ ARTICLE →