Graph graphx_label_propagation

---

graphx_label_propagation(self, max_steps=10, output_vertex_property_name='propagatedLabel')

[ALPHA] Implements the label propagation computation on a graph by invoking graphx api.

Parameters:

max_steps : int32 (default=10)

Number of super-steps before the algorithm terminates. Default = 10

output_vertex_property_name : unicode (default=propagatedLabel)

The name of the column containing the propagated label value.

Returns:

: dict

The original graph with the additional label for each vertex

For detailed information on the algorithm, please see: http://arxiv.org/abs/0709.2938

Examples

>>> graph = ta.Graph()

>>> graph.define_vertex_type('source')
[===Job Progress===]
>>> graph.vertices['source'].add_vertices(vertex_frame, 'source', 'label')
[===Job Progress===]
>>> graph.define_edge_type('edges','source', 'source', directed=False)
[===Job Progress===]
>>> graph.edges['edges'].add_edges(edge_frame, 'source', 'dest', ['weight'])
[===Job Progress===]
>>> result = graph.graphx_label_propagation()
[===Job Progress===]
>>> result['source'].inspect()
    [#]  _vid  _label  source  label  propagatedLabel
    =================================================
    [0]     5  source       5    5.0                5
    [1]     1  source       1    1.0                1
    [2]     2  source       2    1.0                2
    [3]     3  source       3    5.0                2
    [4]     4  source       4    5.0                4

>>> graph.edges['edges'].inspect()
    [#]  _eid  _src_vid  _dest_vid  _label  weight
    ==============================================
    [0]     6         1          2  edges        1
    [1]     7         1          3  edges        1
    [2]     9         1          4  edges        1
    [3]     8         2          3  edges        1
    [4]    10         4          5  edges        1