# Introduction

WebGraph is a framework for graph compression aimed at studying web graphs. It provides simple ways to manage very large graphs, exploiting modern compression techniques. More precisely, it is currently made of:

- A set of flat codes, called
*ζ codes*, which are particularly suitable for storing web graphs (or, in general, integers with power-law distribution in a certain exponent range). The fact that these codes work well can be easily tested empirically, but we also try to provide a detailed mathematical analysis. - Algorithms for compressing web graphs that exploit gap compression and referentiation (à la LINK), intervalisation and ζ codes to provide a high compression ratio (see our datasets). The algorithms are controlled by several parameters, which provide different tradeoffs between access speed and compression ratio.
- Algorithms for accessing a compressed graph without actually decompressing it, using lazy techniques that delay the decompression until it is actually necessary.
- Algorithms for analysing very large graphs, such as HyperBall, which has been used to show that Facebook has just four degrees of separation.
- A complete, documented implementation of the algorithms above in Java distributed under the GNU General Public License. Besides a clearly defined API, we also provide several classes tha modify (e.g., transpose) or recompress a graph, so to experiment with various settings.
- Datasets for very large graph (e.g., a billion of links). These are either gathered from public sources (such as WebBase), or produced by UbiCrawler and BUbiNG.

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, distribution of graph properties of the web graph, etc. very easy.

You are welcome to use and improve WebGraph! If you find our software useful for your research, please quote this paper.

# Installation

You can grab WebGraph from Maven Central.

Otherwise, you have to install the
`.jar` file coming with the distribution and the dependencies, which are gathered for your convenience
in a tarball.

# Hadoop

Helge Holzmann has developed an input format for Hadoop for graphs in BVGraph format.

# WebGraph++

Jacob Ratkievicz has developed a C++ version of WebGraph that you might want to try. The library exposes a BVGraph as an object of the Boost Graph Library, so it is easily integrable with other code.

# pyWebgraph

Massimo Santini has developed a front-end that interfaces Jython with WebGraph. It makes exploring small portions of very large graphs very easy and interactive.