README.md

---
license: other
license_name: commoncrawl-tou
license_link: https://commoncrawl.org/terms-of-use
pretty_name: Common Crawl Web Graph Embeddings
tags:
- common-crawl
- web-graph
- graph-embeddings
- host-graph
- link-prediction
task_categories:
- feature-extraction
size_categories:
- 10M<n<100M
configs:
- config_name: cc_deg8_shallow_v1
  data_files:
  - split: cc_main_2025_26_nov_dec_jan
    path: vectors/emb-*.parquet
---

# Web Graph Embedings from Common Crawl's Host-level Hyperlink Graph

Dense 128-dimensional embeddings for **52,913,544 web hosts**, learned by link prediction on the
Common Crawl **host-level hyperlink graph** release `cc-main-2025-26-nov-dec-jan`. Vectors are **L2-normalized** and served in **float16**;
similarity is **cosine** (a dot product on the unit vectors). 
The dataset contains hosts with **link degree >= 8** (total in+out degree), a 52.9 M / ~19 % induced subgraph that carries ~97 % of the edges.
The source code is available on [Github](https://github.com/commoncrawl/web-graph-embeddings).

This is an experimental dataset release and additional versions are currently not planned.

![graph-embeddings-visual](https://cdn-uploads.huggingface.co/production/uploads/5efda656ff69163f6f59e5d2/NT89oLi9eBr_3Z-DYfjCr.png)


## What each row is


One host, keyed by two stable string identifiers derived deterministically from the Common Crawl
host name (no per-release integer ids — those are ephemeral):

| column | type | meaning |
|---|---|---|
| `row_id` | int64 | global row index; `shard = row_id // rows_per_shard` |
| `host_key` | string | forward host, e.g. `www.example.com` (the stable identity key) |
| `domain_key` | string | registrable domain / PLD (eTLD+1), e.g. `example.com` |
| `embedding` | `fixed_size_list<float16>[128]` | the L2-normalized vector |

## Files

```
vectors/emb-00000-of-00014.parquet … emb-00013-of-00014.parquet   # ~1 GB each, 4 M rows/shard
manifest.json                                                     # schema/model version, per-shard row ranges + SHA-256
```

Shards are **contiguous row ranges** (not hash-partitioned), so `row_id` order is preserved across
shards. To locate one host without downloading everything, read only the `host_key`/`row_id`
columns of the shards (Parquet projects columns), then fetch the single shard
`row_id // 4_000_000`.

## Usage

Stream as a 🤗 `datasets` table:

```python
from datasets import load_dataset
ds = load_dataset("commoncrawl/web-graph-embeddings", "cc_deg8_shallow_v1", split="cc_main_2025_26_nov_dec_jan", streaming=True)
row = next(iter(ds))
print(row["host_key"], len(row["embedding"]))
```

Materialize a contiguous matrix for FAISS / numpy (cosine = inner product on these unit vectors):

```python
import numpy as np, pyarrow.parquet as pq, glob
mats, keys = [], []
for f in sorted(glob.glob("vectors/emb-*.parquet")):
    t = pq.read_table(f, columns=["host_key", "embedding"])
    keys += t["host_key"].to_pylist()
    mats.append(np.asarray(t["embedding"].to_pylist(), dtype=np.float32))
X = np.vstack(mats)                      # [52_913_544, 128], already L2-normalized
```

## Provenance & model

- **Source graph:** Common Crawl host webgraph `cc-main-2025-26-nov-dec-jan`, aggregating crawls
  **CC-MAIN-2025-47, CC-MAIN-2025-51, CC-MAIN-2026-04**.
- **Encoder:** shallow (per-node) embedding table trained by link prediction (WholeGraph-sharded,
  in-batch negatives), `dim=128`, 3 epochs.
- **Link-prediction quality (held-out edges):** MRR **0.957**, Hits@1 **0.939**, Hits@10 **0.984**.


## Explore it in the browser

Two Hugging Face Spaces let you inspect the released vectors without downloading the full dataset.

![2d-viewer-screenshot](https://cdn-uploads.huggingface.co/production/uploads/5efda656ff69163f6f59e5d2/CNDxVBDe9bg4eTuG0MY9Q.png)

**[2D map](https://huggingface.co/spaces/commoncrawl/web-graph-embeddings)** — an interactive
projection of all 52.9 M hosts, streamed by tile. Pan and zoom, hover any point for its hostname
and stats, or jump to one of the top 20k hosts by name. The same layout recolours by link degree,
topic, content language or quality, which makes it easy to see where the embedding separates a
property and where it does not — the view above is coloured by language.

**[Nearest-neighbour search](https://huggingface.co/spaces/commoncrawl/web-graph-knn)** — type a
hostname and get its closest hosts in the full 128-dimensional space rather than in the 2D
projection. All 52,913,544 hosts are indexed. It is the quickest way to check what the embedding
considers "similar" for a site you already know well.


## Evaluation

The model is trained only to predict links, so every result below is a probe of what link structure
alone turns out to encode. Held-out link prediction is near-saturated (`test_mrr 0.9574`); the
interesting question is what transfers.

### Topic — the strongest transfer

A logistic probe on the raw vectors reaches **macro-F1 0.388** over 24 WebOrganizer topics, against
**0.062** for a majority-topic-per-TLD baseline — about **6×** the identity baseline, on 170,399
labelled hosts. Link structure makes a site's topic close to linearly separable.

### Language — near-perfect on well-connected hosts

A single 120-class macro-F1 badly understates this, because it averages over many classes with
almost no held-out support. Stratified by link degree the signal is unambiguous (300,000 held-out
hosts, cosine kNN):

| host degree | accuracy | neighbour-precision | weighted-F1 | macro-F1 @sup≥20 |
|---|--:|--:|--:|--:|
| 8–16 | 0.613 | 0.455 | 0.525 | 0.095 |
| 128–512 | 0.740 | 0.662 | 0.710 | 0.326 |
| 512–4096 | 0.921 | 0.892 | 0.913 | 0.596 |
| ≥ 4096 | **0.980** | **0.970** | **0.976** | **0.713** |

Overall accuracy is 0.673 and weighted-F1 0.612. Mid-resource languages climb from the floor to
strong as degree rises — Spanish 0.02 → 0.45, Polish 0.03 → 0.95, Indonesian 0.02 → 0.83. **The more
links a host has, the more precisely the embedding places its language.**

### Spam — an independent and unusually durable signal

The embedding never saw a spam label. Compared with **Anti-TrustRank** (ATR), the classic link-based
spam algorithm, over 52.6 M candidate hosts:

- **They are not the same signal.** Spearman(embedding, ATR) = **−0.04**.
- **The embedding is the better clean-vs-spam discriminator.** Against operator-reviewed clean
  sites, gold-AUROC is **0.937** for the graph-propagated embedding, **0.874** for a plain logistic
  probe, and 0.865 for ATR.
- **It detects what ATR cannot.** At a 1% false-positive budget against hard, spam-shaped sites that
  reviewers had manually cleared, the embedding finds **40%** of known spam where ATR finds ~0%; at a
  5% budget, **74%** vs ~0%.
- **It cuts false positives 4.5×.** Using ATR for recall and the embedding to vet its top 1,000 drops
  legitimate false positives from 0.122 to **0.027**, holding spam precision at 0.967.
- **It survives a crawl change.** On spam labelled from a *later* crawl, ATR's gold-AUROC falls to
  0.56 — chance — while the embedding still ranks it (enrichment **173×**, AUROC 0.91).

ATR remains the stronger raw ranker on the easy full-web pool (enrichment@1k 117.8×). The practical
recipe is both: ATR for recall, the embedding to vet its hits and to catch the spam that
guilt-by-association misses.

**In one line: link structure alone carries a surprising amount of what a site is** — its topic, its
language wherever the host is well connected, and a spam signal independent of the classic
link-based one.

## Coverage and limitations

- **Only the well-connected core.** Training used hosts with total degree ≥ 8: 52.9 M of the crawl's
  279.4 M hosts (**19%**), covering 13.07 B of ~13.4 B edges. The 81% long tail — hosts with almost
  no links — is *not* in this artifact. There is no vector for a host that was not trained.
- **Transductive.** There is no encoder to apply to a new host; a host absent from the training crawl
  cannot be embedded without retraining.
- **Direction-blind**, as above.
- **One crawl window.** Vectors from different releases are not comparable — the model space is
  arbitrary up to rotation, so a new release means a new space (tracked by `model_space_version` in
  the manifest).
- **Spam metrics are a conservative floor.** The label sets are partial, so unlabelled true spam is
  scored as a false positive; real precision is better than the numbers above.


## License

Derived from the Common Crawl host webgraph. Its use is subject to the
[Common Crawl Terms of Use](https://commoncrawl.org/terms-of-use).