Fixed database typo and removed unnecessary class identifier.
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venv/Lib/site-packages/networkx/algorithms/centrality/katz.py
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venv/Lib/site-packages/networkx/algorithms/centrality/katz.py
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"""Katz centrality."""
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from math import sqrt
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import networkx as nx
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from networkx.utils import not_implemented_for
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__all__ = ["katz_centrality", "katz_centrality_numpy"]
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@not_implemented_for("multigraph")
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def katz_centrality(
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G,
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alpha=0.1,
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beta=1.0,
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max_iter=1000,
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tol=1.0e-6,
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nstart=None,
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normalized=True,
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weight=None,
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):
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r"""Compute the Katz centrality for the nodes of the graph G.
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Katz centrality computes the centrality for a node based on the centrality
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of its neighbors. It is a generalization of the eigenvector centrality. The
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Katz centrality for node $i$ is
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.. math::
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x_i = \alpha \sum_{j} A_{ij} x_j + \beta,
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where $A$ is the adjacency matrix of graph G with eigenvalues $\lambda$.
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The parameter $\beta$ controls the initial centrality and
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.. math::
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\alpha < \frac{1}{\lambda_{\max}}.
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Katz centrality computes the relative influence of a node within a
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network by measuring the number of the immediate neighbors (first
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degree nodes) and also all other nodes in the network that connect
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to the node under consideration through these immediate neighbors.
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Extra weight can be provided to immediate neighbors through the
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parameter $\beta$. Connections made with distant neighbors
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are, however, penalized by an attenuation factor $\alpha$ which
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should be strictly less than the inverse largest eigenvalue of the
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adjacency matrix in order for the Katz centrality to be computed
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correctly. More information is provided in [1]_.
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Parameters
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----------
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G : graph
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A NetworkX graph.
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alpha : float
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Attenuation factor
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beta : scalar or dictionary, optional (default=1.0)
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Weight attributed to the immediate neighborhood. If not a scalar, the
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dictionary must have an value for every node.
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max_iter : integer, optional (default=1000)
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Maximum number of iterations in power method.
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tol : float, optional (default=1.0e-6)
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Error tolerance used to check convergence in power method iteration.
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nstart : dictionary, optional
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Starting value of Katz iteration for each node.
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normalized : bool, optional (default=True)
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If True normalize the resulting values.
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weight : None or string, optional (default=None)
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If None, all edge weights are considered equal.
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Otherwise holds the name of the edge attribute used as weight.
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Returns
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-------
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nodes : dictionary
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Dictionary of nodes with Katz centrality as the value.
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Raises
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------
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NetworkXError
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If the parameter `beta` is not a scalar but lacks a value for at least
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one node
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PowerIterationFailedConvergence
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If the algorithm fails to converge to the specified tolerance
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within the specified number of iterations of the power iteration
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method.
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Examples
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--------
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>>> import math
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>>> G = nx.path_graph(4)
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>>> phi = (1 + math.sqrt(5)) / 2.0 # largest eigenvalue of adj matrix
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>>> centrality = nx.katz_centrality(G, 1 / phi - 0.01)
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>>> for n, c in sorted(centrality.items()):
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... print(f"{n} {c:.2f}")
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0 0.37
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1 0.60
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2 0.60
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3 0.37
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See Also
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--------
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katz_centrality_numpy
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eigenvector_centrality
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eigenvector_centrality_numpy
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pagerank
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hits
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Notes
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-----
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Katz centrality was introduced by [2]_.
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This algorithm it uses the power method to find the eigenvector
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corresponding to the largest eigenvalue of the adjacency matrix of ``G``.
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The parameter ``alpha`` should be strictly less than the inverse of largest
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eigenvalue of the adjacency matrix for the algorithm to converge.
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You can use ``max(nx.adjacency_spectrum(G))`` to get $\lambda_{\max}$ the largest
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eigenvalue of the adjacency matrix.
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The iteration will stop after ``max_iter`` iterations or an error tolerance of
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``number_of_nodes(G) * tol`` has been reached.
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When $\alpha = 1/\lambda_{\max}$ and $\beta=0$, Katz centrality is the same
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as eigenvector centrality.
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For directed graphs this finds "left" eigenvectors which corresponds
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to the in-edges in the graph. For out-edges Katz centrality
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first reverse the graph with ``G.reverse()``.
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References
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----------
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.. [1] Mark E. J. Newman:
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Networks: An Introduction.
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Oxford University Press, USA, 2010, p. 720.
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.. [2] Leo Katz:
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A New Status Index Derived from Sociometric Index.
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Psychometrika 18(1):39–43, 1953
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http://phya.snu.ac.kr/~dkim/PRL87278701.pdf
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"""
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if len(G) == 0:
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return {}
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nnodes = G.number_of_nodes()
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if nstart is None:
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# choose starting vector with entries of 0
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x = {n: 0 for n in G}
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else:
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x = nstart
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try:
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b = dict.fromkeys(G, float(beta))
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except (TypeError, ValueError, AttributeError) as e:
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b = beta
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if set(beta) != set(G):
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raise nx.NetworkXError(
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"beta dictionary " "must have a value for every node"
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) from e
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# make up to max_iter iterations
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for i in range(max_iter):
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xlast = x
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x = dict.fromkeys(xlast, 0)
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# do the multiplication y^T = Alpha * x^T A - Beta
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for n in x:
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for nbr in G[n]:
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x[nbr] += xlast[n] * G[n][nbr].get(weight, 1)
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for n in x:
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x[n] = alpha * x[n] + b[n]
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# check convergence
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err = sum([abs(x[n] - xlast[n]) for n in x])
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if err < nnodes * tol:
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if normalized:
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# normalize vector
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try:
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s = 1.0 / sqrt(sum(v ** 2 for v in x.values()))
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# this should never be zero?
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except ZeroDivisionError:
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s = 1.0
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else:
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s = 1
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for n in x:
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x[n] *= s
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return x
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raise nx.PowerIterationFailedConvergence(max_iter)
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@not_implemented_for("multigraph")
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def katz_centrality_numpy(G, alpha=0.1, beta=1.0, normalized=True, weight=None):
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r"""Compute the Katz centrality for the graph G.
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Katz centrality computes the centrality for a node based on the centrality
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of its neighbors. It is a generalization of the eigenvector centrality. The
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Katz centrality for node $i$ is
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.. math::
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x_i = \alpha \sum_{j} A_{ij} x_j + \beta,
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where $A$ is the adjacency matrix of graph G with eigenvalues $\lambda$.
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The parameter $\beta$ controls the initial centrality and
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.. math::
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\alpha < \frac{1}{\lambda_{\max}}.
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Katz centrality computes the relative influence of a node within a
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network by measuring the number of the immediate neighbors (first
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degree nodes) and also all other nodes in the network that connect
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to the node under consideration through these immediate neighbors.
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Extra weight can be provided to immediate neighbors through the
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parameter $\beta$. Connections made with distant neighbors
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are, however, penalized by an attenuation factor $\alpha$ which
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should be strictly less than the inverse largest eigenvalue of the
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adjacency matrix in order for the Katz centrality to be computed
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correctly. More information is provided in [1]_.
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Parameters
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----------
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G : graph
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A NetworkX graph
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alpha : float
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Attenuation factor
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beta : scalar or dictionary, optional (default=1.0)
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Weight attributed to the immediate neighborhood. If not a scalar the
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dictionary must have an value for every node.
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normalized : bool
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If True normalize the resulting values.
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weight : None or string, optional
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If None, all edge weights are considered equal.
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Otherwise holds the name of the edge attribute used as weight.
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Returns
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-------
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nodes : dictionary
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Dictionary of nodes with Katz centrality as the value.
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Raises
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------
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NetworkXError
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If the parameter `beta` is not a scalar but lacks a value for at least
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one node
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Examples
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--------
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>>> import math
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>>> G = nx.path_graph(4)
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>>> phi = (1 + math.sqrt(5)) / 2.0 # largest eigenvalue of adj matrix
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>>> centrality = nx.katz_centrality_numpy(G, 1 / phi)
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>>> for n, c in sorted(centrality.items()):
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... print(f"{n} {c:.2f}")
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0 0.37
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1 0.60
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2 0.60
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3 0.37
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See Also
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--------
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katz_centrality
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eigenvector_centrality_numpy
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eigenvector_centrality
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pagerank
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hits
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Notes
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-----
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Katz centrality was introduced by [2]_.
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This algorithm uses a direct linear solver to solve the above equation.
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The parameter ``alpha`` should be strictly less than the inverse of largest
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eigenvalue of the adjacency matrix for there to be a solution.
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You can use ``max(nx.adjacency_spectrum(G))`` to get $\lambda_{\max}$ the largest
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eigenvalue of the adjacency matrix.
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When $\alpha = 1/\lambda_{\max}$ and $\beta=0$, Katz centrality is the same
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as eigenvector centrality.
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For directed graphs this finds "left" eigenvectors which corresponds
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to the in-edges in the graph. For out-edges Katz centrality
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first reverse the graph with ``G.reverse()``.
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References
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----------
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.. [1] Mark E. J. Newman:
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Networks: An Introduction.
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Oxford University Press, USA, 2010, p. 720.
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.. [2] Leo Katz:
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A New Status Index Derived from Sociometric Index.
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Psychometrika 18(1):39–43, 1953
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http://phya.snu.ac.kr/~dkim/PRL87278701.pdf
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"""
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try:
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import numpy as np
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except ImportError as e:
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raise ImportError("Requires NumPy: http://numpy.org/") from e
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if len(G) == 0:
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return {}
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try:
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nodelist = beta.keys()
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if set(nodelist) != set(G):
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raise nx.NetworkXError(
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"beta dictionary " "must have a value for every node"
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)
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b = np.array(list(beta.values()), dtype=float)
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except AttributeError:
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nodelist = list(G)
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try:
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b = np.ones((len(nodelist), 1)) * float(beta)
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except (TypeError, ValueError, AttributeError) as e:
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raise nx.NetworkXError("beta must be a number") from e
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A = nx.adj_matrix(G, nodelist=nodelist, weight=weight).todense().T
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n = A.shape[0]
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centrality = np.linalg.solve(np.eye(n, n) - (alpha * A), b)
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if normalized:
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norm = np.sign(sum(centrality)) * np.linalg.norm(centrality)
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else:
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norm = 1.0
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centrality = dict(zip(nodelist, map(float, centrality / norm)))
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return centrality
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