def louvain(input_graph, max_iter=100, resolution=1.):
"""
Compute the modularity optimizing partition of the input graph using the
Louvain heuristic
Parameters
----------
input_graph : cugraph.Graph
cuGraph graph descriptor of type Graph
The adjacency list will be computed if not already present. The graph
should be undirected where an undirected edge is represented by a
directed edge in both direction.
max_iter : integer
This controls the maximum number of levels/iterations of the Louvain
algorithm. When specified the algorithm will terminate after no more
than the specified number of iterations. No error occurs when the
algorithm terminates early in this manner.
resolution: float/double, optional
Called gamma in the modularity formula, this changes the size
of the communities. Defaults to 1.
Returns
-------
parts : cudf.DataFrame
GPU data frame of size V containing two columns the vertex id and the
partition id it is assigned to.
df['vertex'] : cudf.Series
Contains the vertex identifiers
df['partition'] : cudf.Series
Contains the partition assigned to the vertices
modularity_score : float
a floating point number containing the global modularity score of the
partitioning.
Examples
--------
>>> M = cudf.read_csv('datasets/karate.csv',
delimiter = ' ',
dtype=['int32', 'int32', 'float32'],
header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1')
>>> parts, modularity_score = cugraph.louvain(G)
"""
if type(input_graph) is not Graph:
raise Exception("input graph must be undirected")
parts, modularity_score = louvain_wrapper.louvain(input_graph, max_iter,
resolution)
if input_graph.renumbered:
parts = input_graph.unrenumber(parts, "vertex")
return parts, modularity_score
def louvain(input_graph, max_iter=100):
"""
Compute the modularity optimizing partition of the input graph using the
Louvain heuristic
Parameters
----------
input_graph : cugraph.Graph
cuGraph graph descriptor, should contain the connectivity information
as an edge list.
The adjacency list will be computed if not already present. The graph
should be undirected where an undirected edge is represented by a
directed edge in both direction.
max_iter : integer
This controls the maximum number of levels/iterations of the Louvain
algorithm. When specified the algorithm will terminate after no more
than the specified number of iterations. No error occurs when the
algorithm terminates early in this manner.
Returns
-------
parts : cudf.DataFrame
GPU data frame of size V containing two columns the vertex id and the
partition id it is assigned to.
modularity_score : float
a floating point number containing the modularity score of the
partitioning.
Examples
--------
>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>> dtype=['int32', 'int32', 'float32'], header=None)
>>> sources = cudf.Series(M['0'])
>>> destinations = cudf.Series(M['1'])
>>> G = cugraph.Graph()
>>> G.add_edge_list(sources, destinations, None)
>>> parts, modularity_score = cugraph.louvain(G)
"""
if type(input_graph) is not Graph:
raise Exception("input graph must be undirected")
parts, modularity_score = louvain_wrapper.louvain(input_graph,
max_iter=max_iter)
return parts, modularity_score
def louvain(input_graph):
"""
Compute the modularity optimizing partition of the input graph using the
Louvain heuristic
Parameters
----------
input_graph : cugraph.Graph
cuGraph graph descriptor, should contain the connectivity information
as an edge list.
The adjacency list will be computed if not already present. The graph
should be undirected where an undirected edge is represented by a
directed edge in both direction.
Returns
-------
parts : cudf.DataFrame
GPU data frame of size V containing two columns the vertex id and the
partition id it is assigned to.
modularity_score : float
a floating point number containing the modularity score of the
partitioning.
Examples
--------
>>> M = cudf.read_csv('datasets/karate.csv', delimiter=' ',
>>> dtype=['int32', 'int32', 'float32'], header=None)
>>> sources = cudf.Series(M['0'])
>>> destinations = cudf.Series(M['1'])
>>> G = cugraph.Graph()
>>> G.add_edge_list(sources, destinations, None)
>>> parts, modularity_score = cugraph.louvain(G)
"""
parts, modularity_score = louvain_wrapper.louvain(input_graph.graph_ptr)
return parts, modularity_score
def louvain(input_graph, max_iter=100, resolution=1.):
"""
Compute the modularity optimizing partition of the input graph using the
Louvain method
It uses the Louvain method described in:
VD Blondel, J-L Guillaume, R Lambiotte and E Lefebvre: Fast unfolding of
community hierarchies in large networks, J Stat Mech P10008 (2008),
http://arxiv.org/abs/0803.0476
Parameters
----------
input_graph : cugraph.Graph
cuGraph graph descriptor of type Graph
The adjacency list will be computed if not already present.
max_iter : integer
This controls the maximum number of levels/iterations of the Louvain
algorithm. When specified the algorithm will terminate after no more
than the specified number of iterations. No error occurs when the
algorithm terminates early in this manner.
resolution: float/double, optional
Called gamma in the modularity formula, this changes the size
of the communities. Higher resolutions lead to more smaller
communities, lower resolutions lead to fewer larger communities.
Defaults to 1.
Returns
-------
parts : cudf.DataFrame
GPU data frame of size V containing two columns the vertex id and the
partition id it is assigned to.
df['vertex'] : cudf.Series
Contains the vertex identifiers
df['partition'] : cudf.Series
Contains the partition assigned to the vertices
modularity_score : float
a floating point number containing the global modularity score of the
partitioning.
Examples
--------
>>> M = cudf.read_csv('datasets/karate.csv',
delimiter = ' ',
dtype=['int32', 'int32', 'float32'],
header=None)
>>> G = cugraph.Graph()
>>> G.from_cudf_edgelist(M, source='0', destination='1')
>>> parts, modularity_score = cugraph.louvain(G)
"""
if type(input_graph) is not Graph:
raise Exception("input graph must be undirected")
parts, modularity_score = louvain_wrapper.louvain(input_graph, max_iter,
resolution)
if input_graph.renumbered:
parts = input_graph.unrenumber(parts, "vertex")
return parts, modularity_score
