cugraph.analyzeClustering_modularity#
- cugraph.analyzeClustering_modularity(G, n_clusters, clustering, vertex_col_name='vertex', cluster_col_name='cluster')[source]#
Compute the modularity score for a given partitioning/clustering. The assumption is that “clustering” is the results from a call from a special clustering algorithm and contains columns named “vertex” and “cluster”.
- Parameters:
- Gcugraph.Graph or networkx.Graph
graph descriptor. This graph should have edge weights.
- n_clustersinteger
Specifies the number of clusters in the given clustering
- clusteringcudf.DataFrame
The cluster assignment to analyze.
- vertex_col_namestr or list of str, optional (default=’vertex’)
The names of the column in the clustering dataframe identifying the external vertex id
- cluster_col_namestr, optional (default=’cluster’)
The name of the column in the clustering dataframe identifying the cluster id
- Returns:
- scorefloat
The computed modularity score
Examples
>>> from cugraph.datasets import karate >>> G = karate.get_graph(download=True) >>> df = cugraph.spectralBalancedCutClustering(G, 5) >>> score = cugraph.analyzeClustering_modularity(G, 5, df)