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WebGraph

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A Rust implementation of the WebGraph framework for graph compression.

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

  • A set of simple codes, called ζ codes, which are particularly suitable for storing web graphs (or, in general, integers with a power-law distribution in a certain exponent range).

  • Algorithms for compressing web graphs that exploit gap compression and differential compression (à la LINK), intervali<ation, 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 analyzing very large graphs, such as {@link it.unimi.dsi.webgraph.algo.HyperBall}, which has been used to show that Facebook has just four degrees of separation.

  • A Java implementation of the algorithms above, now in maintenance mode.

  • This crate, providing a complete, documented implementation of the algorithms above in Rust. It is free software distributed under either the GNU Lesser General Public License 2.1+ or the Apache Software License 2.0.

  • Data sets for large graphs (e.g., billions of links).

Citation

You are welcome to use and improve WebGraph for your research work! If you find our software useful for research, please cite the following papers in your own:

Quick Setup

Assuming you have built all binaries, you will first need a graph in BV format, for example downloading it from the LAW website. For a graph with basename BASENAME, you will need the BASENAME.graph file (the bitstream containing a compressed representation of the graph), the BASENAME.properties file (metadata) and the BASENAME.offsets file (a bitstream containing pointers into the graph bitstream).

As a first step, if you need random access to the successors of a node, you need to build an Elias–Fano representation of the offsets (this part can be skipped if you just need sequential access). There is a CLI command webgraph with many subcommands, among which build, and webgraph build ef BASENAME will build the representation for you, serializing it with ε-serde in a file named BASENAME.ef.

Then, to load the graph you need to call

let graph = BVGraph::with_basename("BASENAME").load()?;

The with_basename method returns a LoadConfig instance that can be further customized, selecting endianness, type of memory access, and so on. By default you will get big endianness, memory mapping for both the graph and the offsets, and dynamic code dispatch.

Once you load the graph, you can retrieve the successors of a node or iterate on the whole graph. In particular, using the handy for_ macro, you can write an iteration on the graph as

for_!((src, succ) in graph {
    for dst in succ {
        [do something with the arc src -> dst]
    }
});

More Options

  • By starting from the BVGraphSeq class you can obtain an instance that does not need the BASENAME.ef file, but provides only iteration.

  • Graphs can be labeled by zipping them together with a labeling. In fact, graphs are just labelings with usize labels.

Operating on Graphs

There are many operations available on graphs, such as transpose and simplify, and permute.

Acknowledgments

This software has been partially supported by project SERICS (PE00000014) under the NRRP MUR program funded by the EU - NGEU, and by project ANR COREGRAPHIE, grant ANR-20-CE23-0002 of the French Agence Nationale de la Recherche.