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fiedler_vector(G, weight='weight', normalized=False, tol=1e-08, method='tracemin_pcg', seed=None)

The Fiedler vector of a connected undirected graph is the eigenvector corresponding to the second smallest eigenvalue of the Laplacian matrix of the graph.

Notes

Edge weights are interpreted by their absolute values. For MultiGraph's, weights of parallel edges are summed. Zero-weighted edges are ignored.

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: 1e-8)

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_lu' LU factorization =============== ========================================

seed : integer, random_state, or None (default)

Indicator of random number generation state. See Randomness<randomness> .

Raises

NetworkXNotImplemented

If G is directed.

NetworkXError

If G has less than two nodes or is not connected.

Returns

fiedler_vector : NumPy array of floats.

Fiedler vector.

Returns the Fiedler vector of a connected undirected graph.

See Also

laplacian_matrix

Examples

See :

Local connectivity graph

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


GitHub : /networkx/linalg/algebraicconnectivity.py#394
type: <class 'function'>
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