def bimlpa(g_original: object,
theta: float = 0.3,
lambd: int = 7) -> BiNodeClustering:
"""
BiMLPA is designed to detect the many-to-many correspondence community in bipartite networks using multi-label propagation algorithm.
**Supported Graph Types**
========== ======== ======== =========
Undirected Directed Weighted Bipartite
========== ======== ======== =========
Yes No No Yes
========== ======== ======== =========
:param g_original: a networkx/igraph object
:param theta: Label weights threshold. Default 0.3.
:param lambd: The max number of labels. Default 7.
:return: BiNodeClustering object
:Example:
>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.algorithms.bipartite.generators.random_graph(100, 20, 0.1)
>>> coms = algorithms.bimlpa(G)
:References:
Taguchi, Hibiki, Tsuyoshi Murata, and Xin Liu. "BiMLPA: Community Detection in Bipartite Networks by Multi-Label Propagation." International Conference on Network Science. Springer, Cham, 2020.
.. note:: Reference implementation: https://github.com/hbkt/BiMLPA
"""
from BiMLPA import BiMLPA_SqrtDeg, relabeling, output_community
g = convert_graph_formats(g_original, nx.Graph)
if not nx.algorithms.bipartite.is_bipartite(g):
raise ValueError("The graph is not bipartite")
bimlpa = BiMLPA_SqrtDeg(g, theta, lambd)
bimlpa.start()
relabeling(g)
top_coms, bottom_coms = output_community(g)
return BiNodeClustering(
top_coms,
bottom_coms,
g_original,
"BiMLPA",
method_parameters={
"theta": theta,
"lambd": lambd
},
)
def bimlpa(g, theta=0.3, lambd=7):
"""
BiMLPA is designed to detect the many-to-many correspondence community in bipartite networks using multi-label propagation algorithm.
:param g: a networkx/igraph object
:param theta: Label weights threshold. Default 0.3.
:param lambd: The max number of labels. Default 7.
:return: BiNodeClustering object
:Example:
>>> from cdlib import algorithms
>>> import networkx as nx
>>> G = nx.karate_club_graph()
>>> coms = algorithms.bimlpa(G)
:References:
Taguchi, Hibiki, Tsuyoshi Murata, and Xin Liu. "BiMLPA: Community Detection in Bipartite Networks by Multi-Label Propagation." International Conference on Network Science. Springer, Cham, 2020.
.. note:: Reference implementation: https://github.com/hbkt/BiMLPA
"""
from BiMLPA import BiMLPA_SqrtDeg, relabeling, output_community
g = convert_graph_formats(g, nx.Graph)
bimlpa = BiMLPA_SqrtDeg(g, theta, lambd)
bimlpa.start()
relabeling(g)
top_coms, bottom_coms = output_community(g)
return BiNodeClustering(top_coms,
bottom_coms,
g,
"BiMLPA",
method_parameters={
"theta": theta,
"lambd": lambd
})