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WebGraph
WebGraph is a framework to study the web graph. 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),
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
{@link it.unimi.dsi.webgraph.algo.HyperBall}, which
has been used to show that Facebook has just four degrees of separation.
- This package, providing a complete, documented implementation of
the algorithms above in Java. It is free software
distributed under the GNU General Public License.
- Data sets for very large graph (e.g., a billion of links). These are either
gathered from public sources (such as WebBase),
or gathered by UbiCrawler.
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 data set.
You are welcome to use and improve WebGraph! If you find our software useful for your research, please quote
our paper “The WebGraph Framework I: Compression Techniques”, by Paolo Boldi and
Sebastiano Vigna, in Proc. of the Thirteenth World–Wide Web
Conference, pages 595−601, 2004, ACM Press.
Looking around
For in-depth information on the Webgraph framework, you should have
a look at its home page,
where you can find some papers about the compression techniques it uses.
Datasets are available at the
LAW web site.
The classes of interest for the casual Webgraph user are {@link
it.unimi.dsi.webgraph.ImmutableGraph}, which specifies the access
methods for an immutable graph, {@link it.unimi.dsi.webgraph.BVGraph},
which allow to retrieve or recompress a graph stored in the format
described in The WebGraph
Framework I: Compression Techniques, and {@link it.unimi.dsi.webgraph.Transform}, which
provides several ways to transform an {@link it.unimi.dsi.webgraph.ImmutableGraph}.
If you plan on building your graphs dynamically, the class
{@link it.unimi.dsi.webgraph.ArrayListMutableGraph} makes it possible
to create incrementally a graph and then extract an {@linkplain
it.unimi.dsi.webgraph.ArrayListMutableGraph#immutableView() immutable view}.
The package {@link it.unimi.dsi.webgraph.examples} contains useful
examples that show how to access sequentially and randomly an immutable
graph.
Exporting to other formats
{@link it.unimi.dsi.webgraph.ASCIIGraph} and {@link it.unimi.dsi.webgraph.ArcListASCIIGraph}
have main methods that can be used to save an immutable graph, as long as you can load it, in ASCII form.
With data in {@link it.unimi.dsi.webgraph.BVGraph} or {@link it.unimi.dsi.webgraph.EFGraph} format this is as simple as
java -server it.unimi.dsi.webgraph.ASCIIGraph sourcebasename dest
or
java -server it.unimi.dsi.webgraph.ArcListASCIIGraph sourcebasename dest
Please consult the documentation and the command-line help of these two classes to get more information.
Importing your data
If you want to import your own data into WebGraph, you must write
an implementation of {@link it.unimi.dsi.webgraph.ImmutableGraph} that
exposes your data. A simple example is given in {@link it.unimi.dsi.webgraph.examples.IntegerListImmutableGraph},
a stub class exposing a simple, noncompressed binary format as an {@link it.unimi.dsi.webgraph.ImmutableGraph}.
Once your data is exposed in that way, you can get a compressed version
using the store() method of your class of interest. Often, there
is a main method (see, e.g., {@link it.unimi.dsi.webgraph.BVGraph}) that
will load your class and invoke store() for you.
For example, you can use an immutable graph inside the Jung framework using our
{@link it.unimi.dsi.webgraph.jung.JungAdapter}.
As an alternative, the class {@link it.unimi.dsi.webgraph.ASCIIGraph}
can be used to read graphs specified in a very simple ASCII format. The class
implements {@link it.unimi.dsi.webgraph.ASCIIGraph#loadOnce(java.io.InputStream)} so
that the file can be just piped into a class offering a main method that supports
loadOnce() (e.g., {@link it.unimi.dsi.webgraph.BVGraph}).
You can also generate a graph in ASCII format and read it using
{@link it.unimi.dsi.webgraph.ASCIIGraph#loadOffline(CharSequence)}—the
graph will not be loaded into main memory.
{@link it.unimi.dsi.webgraph.ASCIIGraph} requires listing the successors of each
node on a separate line. If your graph is specified arc by arc (one arc per line) you
can use {@link it.unimi.dsi.webgraph.ArcListASCIIGraph} instead.
{@link it.unimi.dsi.webgraph.ShiftedByOneArcListASCIIGraph} can be used if your input
data numbers (rather insensibly) nodes starting from one.
Another possibility is to specify your graph {@linkplain it.unimi.dsi.webgraph.IncrementalImmutableSequentialGraph incrementally}.
which just involves enumerating arrays of successors for each node.
Importing your labelled data
Arc-labelled graphs are represented using implementations of {@link it.unimi.dsi.webgraph.labelling.ArcLabelledImmutableGraph}.
Most arc-labelled graphs are based on an underlying {@link it.unimi.dsi.webgraph.ImmutableGraph}, and
the {@link it.unimi.dsi.webgraph.labelling.ArcLabelledImmutableGraph} implementation just provides
label handling. The example {@link it.unimi.dsi.webgraph.examples.IntegerTriplesArcLabelledImmutableGraph}
shows how to expose your data as an instance of {@link it.unimi.dsi.webgraph.labelling.ArcLabelledImmutableGraph},
so you can save your data using your preferred combination of implementations.