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Expand Up @@ -46,6 +46,11 @@ gathered from public sources (such as
[WebBase](http://www-diglib.stanford.edu/~testbed/doc2/WebBase/)), or
produced by [UbiCrawler](http://law.di.unimi.it/ubicrawler) and BUbiNG.

7. Other large-scale graphs in WebGraph format include [Software-Heritage version-control-history graphs](https://docs.softwareheritage.org/devel/swh-dataset/graph/dataset.html)
and [MS-Biographs sequence similarity graphs](https://dx.doi.org/10.21227/gmd9-1534).

8. Refer to [https://webgraph.di.unimi.it/docs](https://webgraph.di.unimi.it/docs) for WebGraph's documentation.

In the end, with WebGraph you can access and analyse very large web
graphs. Using WebGraph is as easy as installing a few jar files and
downloading a dataset. This makes studying phenomena such as PageRank,
Expand Down Expand Up @@ -86,6 +91,13 @@ that interfaces Jython with
WebGraph](http://code.google.com/p/py-web-graph/). It makes exploring
small portions of very large graphs very easy and interactive.

## ParaGrapher

[ParaGrapher](https://github.com/MohsenKoohi/ParaGrapher) is a C/C++ wrapper for the Java implementation of WebGraph,
facilitating loading large-scale graphs in C/C++ graph processing frameworks.
Additionally, it features PG-Fuse, a user-level filesystem that enhances performance by
loading and caching blocks in larger granularity, particularly when interacting with parallel or distributed file systems like Lustre.

# Papers

* A [detailed description](http://vigna.di.unimi.it/papers.php#BoVWFI) of
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