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May 5, 2024

Host- and Domain-Level Web Graphs November/December 2023, February/March 2024, and April 2024

Note: this post has been marked as obsolete.
We are pleased to announce a new release of host-level and domain-level web graphs based on the crawls of November, February, April 2024.
Thom Vaughan
Thom Vaughan
Thom is Principal Technologist at the Common Crawl Foundation.

We are pleased to announce a new release of host-level and domain-level web graphs based on the crawls of November, February, April 2024. The crawls used to generate the graphs were CC-MAIN-2023-50, CC-MAIN-2024-10, and CC-MAIN-2024-18. Additional information about the data formats, the processing pipeline, our objectives, and credits can be found in the announcements of prior webgraph releases. You may also visit the projects cc-webgraphand cc-pyspark which include all scripts and tools required to construct the graphs. Instructions to explore the graphs in the webgraph format are given in our collection of webgraph notebooks.

Host-level Graph

The host-level graph consists of 335.3 million nodes and 2.7 billion edges.

There are 271.5 million dangling nodes (80.99%) and the largest strongly connected component contains 49.8 million (14.87%) nodes. Dangling nodes stem from:

  • Hosts that have not been crawled, yet are pointed to from a link on a crawled page
  • Hosts without any links pointing to a different host name
  • Hosts which did only return an error page (eg. HTTP 404).

Host names in the graph are in reverse domain name notation and a leading www. is stripped: www.subdomain.example.com becomes com.example.subdomain.

You can download the graph and the ranks of all 335.3 million hosts from AWS S3 on the path s3://commoncrawl/projects/hyperlinkgraph/cc-main-2024-nov-feb-apr/host/ (this requires an account on AWS). Alternatively, you can use https://data.commoncrawl.org/projects/hyperlinkgraph/cc-main-2024-nov-feb-apr/host/ as prefix to access the files from everywhere.

Please note that the text representation of the host-level graph is shipped in 72 gzip-compressed files listed in two path listings - one for the nodes (vertices), one for the edges (arcs). First, download the paths listing and decompress it using "gzip -d" or "gunzip". By adding the prefix s3://commoncrawl/ or https://data.commoncrawl.org/ to each line in the path listing you get the list of URLs to download the entire graph.

Download files of the Common Crawl November, February, April 2024 host-level Webgraph

Size File Description
195 Bytes cc-main-2024-nov-feb-apr-host-vertices.paths.gz nodes ⟨id, rev host⟩, paths of 24 vertices files
258 Bytes cc-main-2024-nov-feb-apr-host-edges.paths.gz edges ⟨from_id, to_id⟩, paths of 48 edges files
5.7 GiB cc-main-2024-nov-feb-apr-host.graph graph in BVGraph format
1.3 KiB cc-main-2024-nov-feb-apr-host.properties
6.0 GiB cc-main-2024-nov-feb-apr-host-t.graph transpose of the graph (outlinks inverted to inlinks)
1.3 KiB cc-main-2024-nov-feb-apr-host-t.properties
816 Bytes cc-main-2024-nov-feb-apr-host.stats WebGraph statistics
5.7 GiB cc-main-2024-nov-feb-apr-host-ranks.txt.gz harmonic centrality and pagerank

Domain-level Graph

The domain graph is built by aggregating the host graph on the level of pay-level domains (PLDs) based on the public suffix list maintained on publicsuffix.org. Version (commit) 0c0a279 of the public suffix list was used (commit date 2024-05-03T09:02:18Z).

The domain-level graph has 113.9 million nodes and 1.9 billion edges. 57.4% or 65.3 million nodes are dangling nodes, the largest strongly connected component covers 40.9 million or 35.96% of the nodes.

All files related to the domain graph are available on AWS S3 under s3://commoncrawl/projects/hyperlinkgraph/cc-main-2024-nov-feb-apr/domain/ or on https://data.commoncrawl.org/projects/hyperlinkgraph/cc-main-2024-nov-feb-apr/domain/.

Download files of the Common Crawl November, February, April 2024 domain-level Webgraph

Credits

Thanks to the authors of the WebGraph framework, whose software made the computation of graph properties and ranks possible. We hope the data will be useful for you to do any kind of research on ranking, graph analysis, link spam detection, etc.

Let us know about your results via Common Crawl's Google Group, or in our Discord Server!

This release was authored by:
Thom is Principal Technologist at the Common Crawl Foundation.
Thom Vaughan