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The Question That Started

It All

Metehan Yesilyurt, an international SEO consultant, had been working with Common Crawl datasets for seven months when he started asking a question that many in the industry have been avoiding: "What about the training data?"

As Metehan put it in his recent research: "Are there reasons why certain domains get recommended so frequently? How can we even find the answer to this?"

The answer, it turns out, was hidden in plain sight within the monthly Web Graph releases. As we shift from traditional Search Engine Optimization (SEO) to Generative Engine Optimization (GEO) and AISEO, understanding the underlying architecture of AI training data isn't just an advantage—it’s a necessity for 2026 and beyond.

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Common Crawl: The Map of the Open Web

To understand AI visibility, you must understand Common Crawl. This non-profit foundation provides open web data to almost every major AI researcher and company worldwide.

Every month, Common Crawl publishes Web Graph Data alongside its main archives. This dataset includes two key authority metrics computed across billions of links:

1. PageRank: The classic measure of a domain’s authority based on the quality and quantity of links pointing to it. 2. Harmonic Centrality (HC): A more nuanced metric that measures how "close" a domain is to all other domains in the Web Graph.

While PageRank is about prestige, Harmonic Centrality is about accessibility. A domain with strong HC can reach many other domains through fewer link "hops." It identifies the central hubs of the internet. Historically, Common Crawl’s crawler (CCBot) has used these metrics to guide its crawling choices.

What’s new is how the SEO community is now applying these metrics to understand AI visibility.

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Why Harmonic Centrality is More Than a Vanity Metric

In the era of AISEO, Harmonic Centrality is becoming the North Star for brand authority. Why? Because of how Large Language Models (LLMs) are built.

The Mozilla Foundation’s 2024 report, “Training Data for the Price of a Sandwich,” confirmed that 64% of the 47 LLMs analyzed used at least one filtered version of Common Crawl data. For GPT-3, over 80% of training tokens originated from these archives.

• Priority: Common Crawl uses Harmonic Centrality to determine crawl priority.
• Frequency: Sites with higher HC scores are crawled more frequently and deeply.
• Representation: More frequent crawling leads to higher representation in the monthly archives.
• Intelligence: Higher representation in the archives means the domain forms a larger part of the LLM’s "worldview" during training.

Here is the logic chain that every CMO should understand:

If your brand isn't central to the web graph, it is effectively invisible to the models being trained today for tomorrow's search.

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Harmonic Centrality vs. PageRank: The New SEO for AI

Traditional SEO often focused on the "linear funnel"—getting a user from a search result to a landing page. AI online presence optimization is different; it’s about becoming a reference point.

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In plain English: It rewards being a "neighbor" to everyone.

If you are linked to by Wikipedia, major news outlets, and niche authority hubs, your distance to the rest of the web shrinks. This makes you a "safe" and "authoritative" source for an LLM to cite when it synthesizes an answer.

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Comparative listicles account for 32.5% of all AI citations, while standard commercial store pages represent a mere 4.73%. This suggests that LLMs prefer "central" nodes that aggregate and compare information over isolated transactional pages.

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How We Measure It: The CC Rank Checker

• Check HC Rank and PageRank: See where you sit in the global hierarchy.
• Track History: See if your authority is growing or shrinking across crawl periods.
• Verify Inclusion: Use the Common Crawl Index Server to confirm exactly which of your pages have been captured by the bot that feeds GPT, Claude, and Llama.

Practitioners no longer have to guess where they stand. Metehan Yesilyurt built the CC Rank Checker Tool, indexing approximately 18 million domains from 2023 to 2025. This allows brands to:

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Practical Applications for Brand Managers

How do you turn this data into a strategy for 2026 SEO?

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The Bigger Picture: From

"Index and Rank" to "Train and Retrieve"

We are witnessing a fundamental shift in how discovery works. The old model was index and rank. The new model is train and retrieve.

In this new paradigm, being in the crawl is a prerequisite for existence. If an LLM hasn't "read" your content during its training phase, or if it doesn't find you via a real-time retrieval-augmented generation (RAG) process, you do not exist to the user.

Common Crawl’s Web Graph Data provides the only transparent lens into how AI models prioritize information. Harmonic Centrality is the metric that bridges the gap between the static web of the past and the generative web of the future.

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What’s Next for AISEO?

As the connection between training data and AI visibility becomes clearer, Harmonic Centrality will move from a niche data-science term to a standard feature in the SEO toolkit. Expect platforms like SEMrush and Ahrefs to eventually integrate these graph metrics into their dashboards.

When optimizing for AI becomes as routine as optimizing for Google, understanding your position in the web's link topology will be table stakes. The brands that win in 2026 will be those that didn't just chase keywords, but worked to become central to the global web graph.

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Ready to bridge the gap between your brand and the models that define the future?