Now the meaning of the bin edges given to the several histogram
functions are the same as in numpy/scipy. This means that the last bin
edge represents an upper bound on the last bin.

This fixes a bug in the motifs() code which generates a segfault for
certain subgraph signatures. This also fixes a potential problem for
multithreaded execution.

This fixes a race condition when undirected graphs are used, and when
OpenMP is enabled.

This fixes a bug where the distance map was not properly initialized
when the graph is filtered, and the all pairs shortest paths are
requested.

This allows for sampling of random subgraph isomorphisms.

Now vector types of property maps can be accessed as arrays with the
get_array() and 'a' property, just like scalar property maps.

This fixes a small typo bug, which prevented loading from file-like
objects.

Edges were not properly indexed after graph generation.

This improves the binning performace, when suplied bins are of constant
width.

absolute_trust() is now called trust_transitivity(), and the whole
trust matrix can now be efficiently calculated.

Vectors are used now instead of unordered_sets, which dramatically
improves performace.

This fixes a bug where edges were left unindexed.

This also maintains compatibility with older boost versions.

This fixes the expected values for several doctests, after
random_graph() re-factoring.

Now the degree sequence is verified with Erdös-Gallai inequalities, and
the edges are connected in a deterministic fashion. The edges then are
rewired with the random_rewire() function.

This includes also some internal refactoring, and bug fixes.

This extensively modifies the random_rewire() algorithm, so that only
either the source or the edge of each edge is rewired (not both, as
previously), and no parallel edges are created during the algorithm (if
desired).

The new version is much faster, and never gets stuck. However, more than
one run may be necessary in order to obtain a uniform shuffling.

This fixes a implicit directionality bug in random_rewire() with
undirected graphs.

This fixes a problem, where the underlying edge directionality would
cause correlations to arise when rewiring undirected graphs. Now each
edge is correctly considered in both possible orientations, which are
chosen randomly.

This implements a shortest_path() function which returns the shortest
path between two vertices.

This also modifies shortest_distances, such that a predecessor map is
optionally returned.

This fixes a crash when purging filtered vertices.