def angel(g_original, threshold, min_community_size=3):
"""
Angel is a node-centric bottom-up community discovery algorithm.
It leverages ego-network structures and overlapping label propagation to identify micro-scale communities that are subsequently merged in mesoscale ones.
Angel is the, faster, successor of Demon.
:param g_original: a networkx/igraph object
:param threshold: merging threshold in [0,1].
:param min_community_size: minimum community size, default 3.
:return: NodeClustering object
:Example:
>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> coms = algorithms.angel(G, min_com_size=3, threshold=0.25)
:References:
1. Rossetti, Giulio. "Exorcising the Demon: Angel, Efficient Node-Centric Community Discovery." International Conference on Complex Networks and Their Applications. Springer, Cham, 2019.
.. note:: Reference implementation: https://github.com/GiulioRossetti/ANGEL
"""
if ig is None:
raise ModuleNotFoundError(
"Optional dependency not satisfied: install igraph to use the selected feature."
)
if Angel is None:
raise ModuleNotFoundError(
"Optional dependency not satisfied: install angel-cd library to use the selected feature (likely pip install angel-cd). If using a notebook, you need also to restart your runtime/kernel."
)
g = convert_graph_formats(g_original, ig.Graph)
with suppress_stdout():
a = Angel(graph=g,
min_comsize=min_community_size,
threshold=threshold,
save=False)
coms = a.execute()
return NodeClustering(list(coms.values()),
g_original,
"ANGEL",
method_parameters={
"threshold": threshold,
"min_community_size": min_community_size
},
overlap=True)
def angel(g, threshold, min_community_size=3):
"""
Angel is a node-centric bottom-up community discovery algorithm.
It leverages ego-network structures and overlapping label propagation to identify micro-scale communities that are subsequently merged in mesoscale ones.
Angel is the, faster, successor of Demon.
:param g: a networkx/igraph object
:param threshold: merging threshold in [0,1].
:param min_community_size: minimum community size, default 3.
:return: NodeClustering object
:Example:
>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> coms = algorithms.angel(G, min_com_size=3, threshold=0.25)
:References:
1. Rossetti G. **Angel: efficient, and effective, node-centric community discovery in static and dynamic networks.**
.. note:: Reference implementation: https://github.com/GiulioRossetti/ANGEL
"""
if ig is None:
raise ModuleNotFoundError(
"Optional dependency not satisfied: install igraph to use the selected feature."
)
g = convert_graph_formats(g, ig.Graph)
with suppress_stdout():
a = Angel(graph=g,
min_comsize=min_community_size,
threshold=threshold,
save=False)
coms = a.execute()
return NodeClustering(list(coms.values()),
g,
"ANGEL",
method_parameters={
"threshold": threshold,
"min_community_size": min_community_size
},
overlap=True)