normalized_laplacian_matrix(G, nodelist=None, weight='weight')
The normalized graph Laplacian is the matrix
$$N = D^{-1/2} L D^{-1/2}$$where :None:None:`L` is the graph Laplacian and :None:None:`D` is the diagonal matrix of node degrees .
For MultiGraph, the edges weights are summed. See to_numpy_array
for other options.
If the Graph contains selfloops, D is defined as diag(sum(A, 1))
, where A is the adjacency matrix .
A NetworkX graph
The rows and columns are ordered according to the nodes in nodelist. If nodelist is None, then the ordering is produced by G.nodes().
The edge data key used to compute each value in the matrix. If None, then each edge has weight 1.
The normalized Laplacian matrix of G.
Returns the normalized Laplacian matrix of G.
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.linalg.laplacianmatrix.laplacian_matrix
networkx.linalg.spectrum.normalized_laplacian_spectrum
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them