We are pleased to announce a new release of host-level and domain-level web graphs based on the published crawls of May, June and July 2019. Additional information about the data formats, the processing pipeline, our objectives, and credits can be found in the announcements of prior webgraph releases (e.g., Nov/Dec/Jan 2017-2018 Webgraphs). You may also visit the projects cc-webgraph and cc-pyspark on GitHub which host all scripts and tools required to construct the graphs.
Links from Content-Location and Link HTTP headers are now also used to span up the web graphs. This is in accordance with RFC 5988 which defines the Link HTTP header as semantically equivalent to the element in HTML. It also fits previous web graph releases which used to include all kinds of links including technical ones and redirects.
The graph consists of 445 million nodes and 3.14 billion edges and includes dangling nodes i.e. hosts that have not been crawled yet are pointed to from a link on a crawled page. There are 382 million dangling nodes (86%) and the largest strongly connected component contains 48 million (11%) nodes.
You can download the graph and the ranks of all 445 million hosts from AWS S3 on the path s3://commoncrawl/projects/hyperlinkgraph/cc-main-2019-may-jun-jul/host/. Alternatively, you can use https://data.commoncrawl.org/projects/hyperlinkgraph/cc-main-2019-may-jun-jul/host/ as prefix to access the files from everywhere.
Download files of the Common Crawl May/June/July 2019 host-level Webgraph
The domain-level graph has 88 million nodes and 1.9 billion edges. 52% or 46 million nodes are dangling nodes, the largest strongly connected component covers 35 million or 40% of the nodes.
All files related to the domain graph are available on AWS S3 under s3://commoncrawl/projects/hyperlinkgraph/cc-main-2019-may-jun-jul/domain/ resp. https://data.commoncrawl.org/projects/hyperlinkgraph/cc-main-2019-may-jun-jul/domain/.
Download files of the Common Crawl May/June/July 2019 domain-level Webgraph
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!