cdlib.evaluation.cut_ratio¶
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cut_ratio
(graph: <Mock id='139632780070864'>, community: object, summary: bool = True) → object¶ Fraction of existing edges (out of all possible edges) leaving the community.
..math:: f(S) = frac{c_S}{n_S (n − n_S)}
where \(c_S\) is the number of community nodes and, \(n_S\) is the number of edges on the community boundary
Parameters: - graph – a networkx/igraph object
- community – NodeClustering object
- summary – boolean. If True it is returned an aggregated score for the partition is returned, otherwise individual-community ones. Default True.
Returns: If summary==True a FitnessResult object, otherwise a list of floats.
Example:
>>> from cdlib.algorithms import louvain >>> from cdlib import evaluation >>> g = nx.karate_club_graph() >>> communities = louvain(g) >>> mod = evaluation.cut_ratio(g,communities)
References: - Fortunato, S.: Community detection in graphs. Physics reports 486(3-5), 75–174 (2010)