Call to random() must be protected by "omp critical".

This returns the union of two graphs, and optionally specified property
maps.

This provides convienent access to the underlying array values, such as:

   p.a = random(len(p.a))

instead of:

   p.get_array()[:] = random(len(p.get_array()))

(the latter is still supported)

This includes generations of releated matrices for spectral analysis,
such as the adjacency matrix, laplacian matrix and incidence matrix.

If output==None, no output is produced, and only the layout positions
are returned.

Random salt should be protected with "omp critical", and edge sampler
should check for the case when all edges have zero probability, and
sample uniformly.

Load 'topology' instead of 'misc'.

This implements a layout module (called 'draw' from python) with an
implementation of the arf spring-block layout.

These functions group several scalar properties into one vector
properties, and ungroup a vector property into several scalar
properties.

It is not necessary to gather the data range if the bin size is
constant. This also changes the histogram code, to better handle the
constant-size bin situation.

This is a large commit which replaces lambda::bind with boost::bind in
most parts of the code. This improves compilation time, and slightly
decreases compilation memory usage in some cases.

This includes the edmonds karp, push relabel and kolmogorov maximum flow
algorithms from the BGL.

Use correct types and always pass GraphWrap objects by value.

This also includes convenience property map checks.

load() function no longer supported in scipy.

No longer only thrown GraphError upon any error, but instead throw
specific exceptions which are more meaninful and are mapped to standard
python exceptions, such as IOError, ValueError and RuntimeError.

Path sampling is done using different bias, and the edge sampling is now
done correctly. There is now only one parameter 'n_iter' which replaces
'min/max_iter' and 'epslon'.