# Source code for networkx.linalg.spectrum

```"""
Eigenvalue spectrum of graphs.
"""
import networkx as nx

__all__ = [
"laplacian_spectrum",
"modularity_spectrum",
"normalized_laplacian_spectrum",
"bethe_hessian_spectrum",
]

[docs]
@nx._dispatchable(edge_attrs="weight")
def laplacian_spectrum(G, weight="weight"):
"""Returns eigenvalues of the Laplacian of G

Parameters
----------
G : graph
A NetworkX graph

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.

Returns
-------
evals : NumPy array
Eigenvalues

Notes
-----
For MultiGraph/MultiDiGraph, the edges weights are summed.
See :func:`~networkx.convert_matrix.to_numpy_array` for other options.

--------
laplacian_matrix

Examples
--------
The multiplicity of 0 as an eigenvalue of the laplacian matrix is equal
to the number of connected components of G.

>>> G = nx.Graph()  # Create a graph with 5 nodes and 3 connected components
>>> nx.laplacian_spectrum(G)
array([0., 0., 0., 2., 2.])

"""
import scipy as sp

return sp.linalg.eigvalsh(nx.laplacian_matrix(G, weight=weight).todense())

[docs]
@nx._dispatchable(edge_attrs="weight")
def normalized_laplacian_spectrum(G, weight="weight"):
"""Return eigenvalues of the normalized Laplacian of G

Parameters
----------
G : graph
A NetworkX graph

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.

Returns
-------
evals : NumPy array
Eigenvalues

Notes
-----
For MultiGraph/MultiDiGraph, the edges weights are summed.
See to_numpy_array for other options.

--------
normalized_laplacian_matrix
"""
import scipy as sp

return sp.linalg.eigvalsh(
nx.normalized_laplacian_matrix(G, weight=weight).todense()
)

[docs]
@nx._dispatchable(edge_attrs="weight")
"""Returns eigenvalues of the adjacency matrix of G.

Parameters
----------
G : graph
A NetworkX graph

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.

Returns
-------
evals : NumPy array
Eigenvalues

Notes
-----
For MultiGraph/MultiDiGraph, the edges weights are summed.
See to_numpy_array for other options.

--------
"""
import scipy as sp

[docs]
@nx._dispatchable
def modularity_spectrum(G):
"""Returns eigenvalues of the modularity matrix of G.

Parameters
----------
G : Graph
A NetworkX Graph or DiGraph

Returns
-------
evals : NumPy array
Eigenvalues

--------
modularity_matrix

References
----------
.. [1] M. E. J. Newman, "Modularity and community structure in networks",
Proc. Natl. Acad. Sci. USA, vol. 103, pp. 8577-8582, 2006.
"""
import scipy as sp

if G.is_directed():
return sp.linalg.eigvals(nx.directed_modularity_matrix(G))
else:
return sp.linalg.eigvals(nx.modularity_matrix(G))

[docs]
@nx._dispatchable
def bethe_hessian_spectrum(G, r=None):
"""Returns eigenvalues of the Bethe Hessian matrix of G.

Parameters
----------
G : Graph
A NetworkX Graph or DiGraph

r : float
Regularizer parameter

Returns
-------
evals : NumPy array
Eigenvalues

--------
bethe_hessian_matrix

References
----------
.. [1] A. Saade, F. Krzakala and L. Zdeborová
"Spectral clustering of graphs with the bethe hessian",
Advances in Neural Information Processing Systems. 2014.
"""
import scipy as sp

return sp.linalg.eigvalsh(nx.bethe_hessian_matrix(G, r).todense())

```