Propagation
This notebook illustrates the clustering of a graph by label propagation.
[1]:
from IPython.display import SVG
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import numpy as np
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from sknetwork.data import karate_club, painters, movie_actor
from sknetwork.clustering import PropagationClustering, get_modularity
from sknetwork.linalg import normalize
from sknetwork.utils import get_membership
from sknetwork.visualization import visualize_graph, visualize_bigraph
Graphs
[4]:
graph = karate_club(metadata=True)
adjacency = graph.adjacency
position = graph.position
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propagation = PropagationClustering()
labels = propagation.fit_predict(adjacency)
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labels_unique, counts = np.unique(labels, return_counts=True)
print(labels_unique, counts)
[0 1] [19 15]
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image = visualize_graph(adjacency, position, labels=labels)
SVG(image)
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[8]:
# metric
get_modularity(adjacency, labels)
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0.35231755424063116
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# aggregate graph
adjacency_aggregate = propagation.aggregate_
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average = normalize(get_membership(labels).T)
position_aggregate = average.dot(position)
labels_unique, counts = np.unique(labels, return_counts=True)
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image = visualize_graph(adjacency_aggregate, position_aggregate, counts, labels=labels_unique,
display_node_weight=True, node_weights=counts)
SVG(image)
[11]:
[12]:
# probability distribution over clusters
probs = propagation.predict_proba()
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# scores for cluster 1
scores = probs[:,1]
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image = visualize_graph(adjacency, position, scores=scores)
SVG(image)
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Directed graphs
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graph = painters(metadata=True)
adjacency = graph.adjacency
names = graph.names
position = graph.position
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propagation = PropagationClustering()
labels = propagation.fit_predict(adjacency)
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labels_unique, counts = np.unique(labels, return_counts=True)
print(labels_unique, counts)
[0 1] [10 4]
[18]:
image = visualize_graph(adjacency, position, names=names, labels=labels)
SVG(image)
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[19]:
get_modularity(adjacency, labels)
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0.256
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# aggregate graph
adjacency_aggregate = propagation.aggregate_
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average = normalize(get_membership(labels).T)
position_aggregate = average.dot(position)
labels_unique, counts = np.unique(labels, return_counts=True)
[22]:
image = visualize_graph(adjacency_aggregate, position_aggregate, counts, labels=labels_unique,
display_node_weight=True, node_weights=counts)
SVG(image)
[22]:
[23]:
# probability distribution over clusters
probs = propagation.predict_proba()
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# scores for cluster 1
scores = probs[:,1]
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image = visualize_graph(adjacency, position, scores=scores)
SVG(image)
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Bipartite graphs
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graph = movie_actor(metadata=True)
biadjacency = graph.biadjacency
names_row = graph.names_row
names_col = graph.names_col
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propagation = PropagationClustering()
propagation.fit(biadjacency)
labels_row = propagation.labels_row_
labels_col = propagation.labels_col_
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image = visualize_bigraph(biadjacency, names_row, names_col, labels_row, labels_col)
SVG(image)
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[29]:
get_modularity(biadjacency, labels_row, labels_col)
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0.4138321995464852
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# aggregate graph
biadjacency_aggregate = propagation.aggregate_
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labels_unique_row, counts_row = np.unique(labels_row, return_counts=True)
labels_unique_col, counts_col = np.unique(labels_col, return_counts=True)
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image = visualize_bigraph(biadjacency_aggregate, counts_row, counts_col, labels_unique_row, labels_unique_col,
display_node_weight=True, node_weights_row=counts_row, node_weights_col=counts_col)
SVG(image)
[32]:
[33]:
# probability distribution over clusters
probs_row = propagation.predict_proba()
probs_col = propagation.predict_proba(columns=True)
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# soft clustering
scores_row = probs_row[:,1]
scores_col = probs_col[:,1]
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image = visualize_bigraph(biadjacency, names_row, names_col, scores_row=scores_row, scores_col=scores_col)
SVG(image)
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