cdlib.viz.plot_network_clusters

plot_network_clusters(graph: object, partition: cdlib.classes.node_clustering.NodeClustering, position: dict = None, figsize: tuple = (8, 8), node_size: int = 200, plot_overlaps: bool = False, plot_labels: bool = False, cmap: object = None, top_k: int = None, min_size: int = None) → object

Plot a graph with node color coding for communities.

Parameters:

• graph – NetworkX/igraph graph
• partition – NodeClustering object
• position – A dictionary with nodes as keys and positions as values. Example: networkx.fruchterman_reingold_layout(G). By default, uses nx.spring_layout(g)
• figsize – the figure size; it is a pair of float, default (8, 8)
• node_size – int, default 200
• plot_overlaps – bool, default False. Flag to control if multiple algorithms memberships are plotted.
• plot_labels – bool, default False. Flag to control if node labels are plotted.
• cmap – str or Matplotlib colormap, Colormap(Matplotlib colormap) for mapping intensities of nodes. If set to None, original colormap is used.
• top_k – int, Show the top K influential communities. If set to zero or negative value indicates all.
• min_size – int, Exclude communities below the specified minimum size.

Example:

>>> from cdlib import algorithms, viz
>>> import networkx as nx
>>> g = nx.karate_club_graph()
>>> coms = algorithms.louvain(g)
>>> pos = nx.spring_layout(g)
>>> viz.plot_network_clusters(g, coms, pos)