cdlib.evaluation.average_internal_degree¶
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average_internal_degree
(graph: <Mock id='140350750933904'>, community: object, summary: bool = True) → object¶ The average internal degree of the community set.
\[ \begin{align}\begin{aligned}f(S) = \frac{2m_S}{n_S}\\where :math:`m_S` is the number of community internal edges and :math:`n_S` is the number of community nodes.\end{aligned}\end{align} \]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.average_internal_degree(g,communities)
References: - Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004). Defining and identifying communities in networks. Proceedings of the National Academy of Sciences, 101(9), 2658-2663.