directed_laplacian_matrix¶

directed_laplacian_matrix(G, nodelist=None, weight='weight', walk_type=None, alpha=0.95)[source]¶

Return the directed Laplacian matrix of G.

The graph directed Laplacian is the matrix

$L = I - (\Phi^{1/2} P \Phi^{-1/2} + \Phi^{-1/2} P^T \Phi^{1/2} ) / 2$

where $I$ is the identity matrix, $P$ is the transition matrix of the graph, and $\Phi$ a matrix with the Perron vector of $P$ in the diagonal and zeros elsewhere.

Depending on the value of walk_type, $P$ can be the transition matrix induced by a random walk, a lazy random walk, or a random walk with teleportation (PageRank).

Parameters :

G : DiGraph

A NetworkX graph

nodelist : list, optional

The rows and columns are ordered according to the nodes in nodelist. If nodelist is None, then the ordering is produced by G.nodes().

weight : string or None, optional (default=’weight’)

The edge data key used to compute each value in the matrix. If None, then each edge has weight 1.

walk_type : string or None, optional (default=None)

If None, $P$ is selected depending on the properties of the graph. Otherwise is one of ‘random’, ‘lazy’, or ‘pagerank’

alpha : real

(1 - alpha) is the teleportation probability used with pagerank

Returns :

L : NumPy array

Normalized Laplacian of G.

Raises :

NetworkXError :

If NumPy cannot be imported

NetworkXNotImplemnted :

If G is not a DiGraph

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