muspan.networks#

The muspan.networks module provides tools for working with networks in the MuSpAn package. This module includes the following submodules:

Network generation and manipulation

`generate_network`	Generate network using the domain objects.
`add_similarity_weight_to_edges`	Adds edge weight to all connected nodes that is valued as a similarity of node features.
`add_dissimilarity_weight_to_edges`	Adds edge weight to all connected nodes that is valued as a dissimilarity of node features.

Neighbourhood analysis methods

`cluster_neighbourhoods`	A function to cluster neighbourhoods based on the enrichment of labels within the neighbourhoods of objects using a network connectivity.
`khop_neighbourhood`	For a given network, find the k-hop neighbourhood of a set of source objects.
`knn_label_count`	Counts the number of each label within proximity of a source object using KNN networks.

Network-based point pattern analysis

Test the correlation between adjacency labels in a network, also referred to as neighbourhood enrichment.

Network metrics

`alpha_index`	Calculate the alpha index of a network.
`betweenness_centrality`	Computes the betweenness centrality of each node using the networkx implementation.
`closeness_centrality`	Computes the closeness centrality of each node using the networkx implementation.
`compactness`	Provides an index [0,1] of how compact the network is in terms of its area and length of edges.
`cyclomatic_number`	Calculate the cyclomatic number of a network.
`edge_orientation_distribution`	Computes the symmetric distribution of edge orientations for a network.
`gamma_index`	The gamma index is a simple measure of the density of the network.
`katz_centrality`	Computes the Katz centrality of each node using the networkx implementation.
`laplacian_centrality`	Computes the laplacian centrality of each node.
`node_degree`	Computes the degree for each node.

Network clustering

Community detection on a provided network.

Network comparison methods

Calculate the distance between two networks providing a quantitative measure of the similarity between them.