katz_centrality(G, alpha=0.1, beta=1.0, max_iter=1000, tol=1e-06, nstart=None, normalized=True, weight='weight')¶
Compute the Katz centrality for the nodes of the graph G.
Katz centrality computes the centrality for a node based on the centrality of its neighbors. It is a generalization of the eigenvector centrality. The Katz centrality for node is
where is the adjacency matrix of the graph G with eigenvalues .
The parameter controls the initial centrality and
Katz centrality computes the relative influence of a node within a network by measuring the number of the immediate neighbors (first degree nodes) and also all other nodes in the network that connect to the node under consideration through these immediate neighbors.
Extra weight can be provided to immediate neighbors through the parameter . Connections made with distant neighbors are, however, penalized by an attenuation factor which should be strictly less than the inverse largest eigenvalue of the adjacency matrix in order for the Katz centrality to be computed correctly. More information is provided in  .
- G (graph) – A NetworkX graph
- alpha (float) – Attenuation factor
- beta (scalar or dictionary, optional (default=1.0)) – Weight attributed to the immediate neighborhood. If not a scalar, the dictionary must have an value for every node.
- max_iter (integer, optional (default=1000)) – Maximum number of iterations in power method.
- tol (float, optional (default=1.0e-6)) – Error tolerance used to check convergence in power method iteration.
- nstart (dictionary, optional) – Starting value of Katz iteration for each node.
- normalized (bool, optional (default=True)) – If True normalize the resulting values.
- weight (None or string, optional) – If None, all edge weights are considered equal. Otherwise holds the name of the edge attribute used as weight.
nodes – Dictionary of nodes with Katz centrality as the value.
NetworkXError– If the parameter is not a scalar but lacks a value for at least one node
>>> import math >>> G = nx.path_graph(4) >>> phi = (1+math.sqrt(5))/2.0 # largest eigenvalue of adj matrix >>> centrality = nx.katz_centrality(G,1/phi-0.01) >>> for n,c in sorted(centrality.items()): ... print("%d %0.2f"%(n,c)) 0 0.37 1 0.60 2 0.60 3 0.37
Katz centrality was introduced by .
This algorithm it uses the power method to find the eigenvector corresponding to the largest eigenvalue of the adjacency matrix of G. The constant alpha should be strictly less than the inverse of largest eigenvalue of the adjacency matrix for the algorithm to converge. The iteration will stop after max_iter iterations or an error tolerance of number_of_nodes(G)*tol has been reached.
When and , Katz centrality is the same as eigenvector centrality.
For directed graphs this finds “left” eigenvectors which corresponds to the in-edges in the graph. For out-edges Katz centrality first reverse the graph with G.reverse().
 Mark E. J. Newman: Networks: An Introduction. Oxford University Press, USA, 2010, p. 720.  Leo Katz: A New Status Index Derived from Sociometric Index. Psychometrika 18(1):39–43, 1953 http://phya.snu.ac.kr/~dkim/PRL87278701.pdf