This enables the fast computation of unbiased estimators.

Now algorithms rely more on parallel_{vertex/edge}_loop() and global
the paralellization threshold is honored.

This also implements direct manipulation of OpenMP settings (schedule
and number of threads) via python.

Use schedule(static, 100), to reduce overhead with small graphs in the
majority of cases.

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.

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.

Fix bad_any_cast bug when passing a weight property.

Thins changes the graph filtering code slightly to wrap graph types with
GraphWrap, which automatically updates the edge index list when edges
are removed and added to the graph.

This also changes how graphs are passed to algorithms, which is now by
reference instead of pointer. (hence this touches lots of code, but
changes are trivial)

This adds the centrality module, which supports betweenness, eigentrust,
pagerank and absolute trust.