We are pleased to announce a new release of host-level and domain-level web graphs based on the published crawls of August, September and October 2018. Additional information about data formats, the processing pipeline, our objectives, and credits can be found in the announcements of prior webgraph releases (e.g., the Feb/Mar/Apr 2017 Webgraphs). You may also visit the projects cc-webgraph and cc-pyspark which host all scripts and tools required to construct the graphs.
Host-level graph
The graph consists of 903 million nodes and 5.25 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 819 million dangling nodes (91%) and the largest strongly connected component contains only 60 million (6.5%) nodes. The host names are reversed and a leading www. is stripped: www.subdomain.example.com becomes com.example.subdomain.
You can download the graph and the ranks of all 903 million hosts from AWS S3 on the path s3://commoncrawl/projects/hyperlinkgraph/cc-main-2018-aug-sep-oct/host/. Alternatively, you can use https://data.commoncrawl.org/projects/hyperlinkgraph/cc-main-2018-aug-sep-oct/host/ as prefix to access the files from everywhere.
The following files and formats are provided:
Domain-level graph
The domain graph was built by aggregating the host graph on the level of pay-level domains (PLDs) based on the public suffix list maintained on publicsuffix.org.
The domain-level graph has 87 million nodes and 1.48 billion edges. 56% or 49 million nodes are dangling nodes, the largest strongly connected component covers 33.5 million or 38% of the nodes.
All files related to the domain graph are available on AWS S3 under s3://commoncrawl/projects/hyperlinkgraph/cc-main-2018-aug-sep-oct/domain/ resp. https://data.commoncrawl.org/projects/hyperlinkgraph/cc-main-2018-aug-sep-oct/domain/.
Download files of the Common Crawl Aug/Sep/Oct 2018 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!
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