networkx.linalg.algebraicconnectivity.fiedler_vector¶

fiedler_vector
(G, weight='weight', normalized=False, tol=1e08, method='tracemin_pcg', seed=None)[source]¶ Returns the Fiedler vector of a connected undirected graph.
The Fiedler vector of a connected undirected graph is the eigenvector corresponding to the second smallest eigenvalue of the Laplacian matrix of of the graph.
Parameters: G (NetworkX graph) – An undirected graph.
weight (object, optional (default: None)) – The data key used to determine the weight of each edge. If None, then each edge has unit weight.
normalized (bool, optional (default: False)) – Whether the normalized Laplacian matrix is used.
tol (float, optional (default: 1e8)) – Tolerance of relative residual in eigenvalue computation.
method (string, optional (default: ‘tracemin_pcg’)) – Method of eigenvalue computation. It must be one of the tracemin options shown below (TraceMIN), ‘lanczos’ (Lanczos iteration) or ‘lobpcg’ (LOBPCG).
The TraceMIN algorithm uses a linear system solver. The following values allow specifying the solver to be used.
Value Solver ‘tracemin_pcg’ Preconditioned conjugate gradient method ‘tracemin_chol’ Cholesky factorization ‘tracemin_lu’ LU factorization seed (integer, random_state, or None (default)) – Indicator of random number generation state. See Randomness.
Returns: fiedler_vector – Fiedler vector.
Return type: NumPy array of floats.
Raises: NetworkXNotImplemented
– If G is directed.NetworkXError
– If G has less than two nodes or is not connected.
Notes
Edge weights are interpreted by their absolute values. For MultiGraph’s, weights of parallel edges are summed. Zeroweighted edges are ignored.
To use Cholesky factorization in the TraceMIN algorithm, the
scikits.sparse
package must be installed.See also
laplacian_matrix()