def sbm_dl_nested(g, B_min=None, B_max=None, deg_corr=True, **kwargs):
"""Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. (nested)
Fit a nested non-overlapping stochastic block model (SBM) by minimizing its description length using an agglomerative heuristic.
Return the lowest level found. Currently cdlib do not support hierarchical clustering.
If no parameter is given, the number of blocks will be discovered automatically. Bounds for the number of communities can
be provided using B_min, B_max.
:param B_min: minimum number of communities that can be found
:param B_max: maximum number of communities that can be found
:param deg_corr: if true, use the degree corrected version of the SBM
:return: NodeClustering object
:Example:
>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> coms = sbm_dl(G)
:References:
Tiago P. Peixoto, “Hierarchical block structures and high-resolution model selection in large networks”, Physical Review X 4.1 (2014): 011047
.. note:: Use implementation from graph-tool library, please report to https://graph-tool.skewed.de for details
"""
if gt is None:
raise Exception(
"===================================================== \n"
"The graph-tool library seems not to be installed (or incorrectly installed). \n"
"Please check installation procedure there https://git.skewed.de/count0/graph-tool/wikis/installation-instructions#native-installation \n"
"on linux/mac, you can use package managers to do so(apt-get install python3-graph-tool, brew install graph-tool, etc.)"
)
gt_g = convert_graph_formats(g, nx.Graph)
gt_g, label_map = __from_nx_to_graph_tool(gt_g)
state = gt.minimize_nested_blockmodel_dl(gt_g,
B_min,
B_max,
deg_corr=deg_corr)
level0 = state.get_levels()[0]
affiliations = level0.get_blocks().get_array()
affiliations = {
label_map[i]: affiliations[i]
for i in range(len(affiliations))
}
coms = affiliations2nodesets(affiliations)
coms = [list(v) for k, v in coms.items()]
return NodeClustering(coms,
g,
"SBM_nested",
method_parameters={
"B_min": B_min,
"B_max": B_max,
"deg_corr": deg_corr
})
def sbm_dl(g_original, B_min=None,B_max=None, deg_corr=True, **kwargs):
"""Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models.
Fit a non-overlapping stochastic block model (SBM) by minimizing its description length using an agglomerative heuristic.
If no parameter is given, the number of blocks will be discovered automatically. Bounds for the number of communities can
be provided using B_min, B_max.
:param g_original: network/igraph object
:param B_min: minimum number of communities that can be found
:param B_max: maximum number of communities that can be found
:param deg_corr: if true, use the degree corrected version of the SBM
:return: NodeClustering object
:Example:
>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> coms = sbm_dl(G)
:References:
Tiago P. Peixoto, “Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models”, Phys. Rev. E 89, 012804 (2014), DOI: 10.1103/PhysRevE.89.012804 [sci-hub, @tor], arXiv: 1310.4378.
.. note:: Use implementation from graph-tool library, please report to https://graph-tool.skewed.de for details
"""
if gt is None:
raise Exception("===================================================== \n"
"The graph-tool library seems not to be installed (or incorrectly installed). \n"
"Please check installation procedure there https://git.skewed.de/count0/graph-tool/wikis/installation-instructions#native-installation \n"
"on linux/mac, you can use package managers to do so(apt-get install python3-graph-tool, brew install graph-tool, etc.)")
gt_g = convert_graph_formats(g_original, nx.Graph)
gt_g, label_map = __from_nx_to_graph_tool(gt_g)
state = gt.minimize_blockmodel_dl(gt_g, B_min, B_max, deg_corr=deg_corr)
affiliations = state.get_blocks().get_array()
affiliations = {label_map[i]: affiliations[i] for i in range(len(affiliations))}
coms = affiliations2nodesets(affiliations)
coms = [list(v) for k,v in coms.items()]
return NodeClustering(coms, g_original, "SBM", method_parameters={"B_min": B_min, "B_max": B_max, "deg_corr": deg_corr})
