combined_corr_hist

graph_tool.correlations.combined_corr_hist(g, deg1, deg2, bins=[[0, 1], [0, 1]], float_count=True)

Obtain the single-vertex combined correlation histogram for the given graph.

Parameters:

gGraph

Graph to be used.

deg1string or VertexPropertyMap

first degree type (“in”, “out” or “total”) or vertex property map.

deg2string or VertexPropertyMap

second degree type (“in”, “out” or “total”) or vertex property map.

binslist of lists (optional, default: [[0, 1], [0, 1]])

A list of bin edges to be used for the first and second degrees. If any list has size 2, it is used to create an automatically generated bin range starting from the first value, and with constant bin width given by the second value.

float_countbool (optional, default: True)

If True, the bin counts are converted float variables, which is useful for normalization, and other processing. It False, the bin counts will be unsigned integers.

Returns:

bin_countsnumpy.ndarray

Two-dimensional array with the bin counts.

first_binsnumpy.ndarray

First degree bins

second_binsnumpy.ndarray

Second degree bins

See also

assortativity

assortativity coefficient

scalar_assortativity

scalar assortativity coefficient

corr_hist

vertex-vertex correlation histogram

combined_corr_hist

combined single-vertex correlation histogram

avg_neighbor_corr

average nearest-neighbor correlation

avg_combined_corr

average combined single-vertex correlation

Notes

Parallel implementation.

If enabled during compilation, this algorithm will run in parallel using OpenMP. See the parallel algorithms section for information about how to control several aspects of parallelization.

Examples

>>> def sample_k(max):
...     accept = False
...     while not accept:
...         i = np.random.randint(1, max + 1)
...         j = np.random.randint(1, max + 1)
...         accept = np.random.random() < (sin(i / pi) * sin(j / pi) + 1) / 2
...     return i,j
...
>>> g = gt.random_graph(10000, lambda: sample_k(40))
>>> h = gt.combined_corr_hist(g, "in", "out")
>>> clf()
>>> xlabel("In-degree")
Text(...)
>>> ylabel("Out-degree")
Text(...)
>>> imshow(h[0].T, interpolation="nearest", origin="lower")
<...>
>>> colorbar()
<...>
>>> savefig("combined_corr.svg")

Combined in/out-degree correlation histogram.