textnets.network.TextnetBase¶

class textnets.network.TextnetBase(graph: Graph)[source]¶

Bases: ABC

Abstract base class for Textnet and ProjectedTextnet.

graph¶

Direct access to the igraph object.

Methods

`ecount`	Return the number of edges.
`plot`
`save_graph`	Save the underlying graph.
`top_cluster_nodes`	Show top nodes per cluster, ranked by a chosen metric.
`top_degree`	Show nodes sorted by unweighted degree.
`top_strength`	Show nodes sorted by weighted degree.
`vcount`	Return the number of vertices (nodes).

Attributes

`cluster_local_cc`	Weighted local clustering coefficient within each cluster's subgraph.
`cluster_strength`	Weighted node degree within each cluster's subgraph.
`clusters`	Return graph partition.
`degree`	Unweighted node degree.
`edges`	Iterate of edges.
`modularity`	Return modularity based on graph partition.
`node_types`	Return list of node types.
`nodes`	Iterate over nodes.
`strength`	Weighted node degree.
`summary`	Summary of underlying graph.

property cluster_local_cc: Series¶: Weighted local clustering coefficient within each cluster’s subgraph.

property cluster_strength: Series¶: Weighted node degree within each cluster’s subgraph.

property clusters: VertexClustering¶

Return graph partition.

The partition is detected by the Leiden algorithm, unless a different partition that was supplied to the setter.

save_graph(target: str | bytes | PathLike[Any] | IO, format: str | None = None) → None[source]¶

Save the underlying graph.

Parameters:

target (str or path or file) – File or path that the graph should be written to.
format ({"dot", "edgelist", "gml", "graphml", "pajek", ...}, optional) – Optionally specify the desired format (otherwise it is inferred from the file suffix).

top_cluster_nodes(n: int = 10, rank_nodes_by: str = 'cluster_strength') → DataFrame[source]¶

Show top nodes per cluster, ranked by a chosen metric.

Parameters:

n (int, optional) – How many nodes to show per cluster (default: 10)
rank_nodes_by (str, optional) – Metric to rank nodes within each cluster by (default: cluster_strength).

Returns:

Clusters with representative nodes.

Return type:

pandas.DataFrame

top_degree(n=10)¶

Show nodes sorted by unweighted degree.

top_strength(n=10)¶

Show nodes sorted by weighted degree.