This completes all the documentation up to this point.

This dumps the random-path approach, and implements a deterministic,
priority queue algorithm.

This fixes an obvious problem, where the graph gets deleted, and the
descriptors are still lying around. Usage of orphaned descriptors will
now just raise a ValueError.

The __repr__ function of Edge, Vertex, and PropertyMap now give
something more informative about each object.

In both functions we can save some time by hashing the subgraphs found,
according to their degree distribution signature (instead of number of
edges). This avoids a large number of useless exactness or isomorphism
comparisons.

This also removes the "seed" parameter, which is redundant to
the numpy.random.seed() function.

Mostly in the flow and spectral modules.

The file graph_predecessor.hh was missing.

The value "random" should be "uncorrelated" instead.

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.