@@ -63,7 +63,7 @@ To apply one of the first three strategies, use the following snippet:
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.. code-block :: python
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- from cdlib import LifeCycle
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+ from cdlib import LifeCycle, TemporalClustering, algorithms
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from networkx.generators.community import LFR_benchmark_graph
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tc = TemporalClustering()
@@ -92,7 +92,7 @@ To define custom events, use the following snippet:
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.. code-block :: python
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- from cdlib import LifeCycle
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+ from cdlib import LifeCycle, TemporalClustering, algorithms
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from networkx.generators.community import LFR_benchmark_graph
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tc = TemporalClustering()
@@ -143,7 +143,7 @@ Here an example of how to analyze community events and flows:
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.. code-block :: python
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- from cdlib import LifeCycle
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+ from cdlib import LifeCycle, TemporalClustering, algorithms
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from networkx.generators.community import LFR_benchmark_graph
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tc = TemporalClustering()
@@ -186,7 +186,7 @@ Setting and retreiving node attributes is straightforward:
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.. code-block :: python
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- from cdlib import LifeCycle
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+ from cdlib import LifeCycle, TemporalClustering, algorithms
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from networkx.generators.community import LFR_benchmark_graph
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def random_leaning ():
@@ -236,7 +236,7 @@ Here an example of how to visualize community events, flows and polytree:
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.. code-block :: python
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- from cdlib import LifeCycle
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+ from cdlib import LifeCycle, TemporalClustering, algorithms
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from cdlib.viz import (
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plot_flow,
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plot_event_radar,
@@ -290,7 +290,7 @@ Here an example of how to validate the observed flows:
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.. code-block :: python
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- from cdlib import LifeCycle
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+ from cdlib import LifeCycle, TemporalClustering, algorithms
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from cdlib.lifecycles.validation import validate_flow, validate_all_flows
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from networkx.generators.community import LFR_benchmark_graph
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