# networkx.algorithms.bipartite.centrality.degree_centrality¶

degree_centrality(G, nodes)[source]

Compute the degree centrality for nodes in a bipartite network.

The degree centrality for a node v is the fraction of nodes connected to it.

Parameters
• G (graph) – A bipartite network

• nodes (list or container) – Container with all nodes in one bipartite node set.

Returns

centrality – Dictionary keyed by node with bipartite degree centrality as the value.

Return type

dictionary

betweenness_centrality(), closeness_centrality(), sets(), is_bipartite()

Notes

The nodes input parameter must contain all nodes in one bipartite node set, but the dictionary returned contains all nodes from both bipartite node sets. See bipartite documentation for further details on how bipartite graphs are handled in NetworkX.

For unipartite networks, the degree centrality values are normalized by dividing by the maximum possible degree (which is n-1 where n is the number of nodes in G).

In the bipartite case, the maximum possible degree of a node in a bipartite node set is the number of nodes in the opposite node set 1. The degree centrality for a node v in the bipartite sets U with n nodes and V with m nodes is

\begin{align}\begin{aligned}d_{v} = \frac{deg(v)}{m}, \mbox{for} v \in U ,\\d_{v} = \frac{deg(v)}{n}, \mbox{for} v \in V ,\end{aligned}\end{align}

where deg(v) is the degree of node v.

References

1

Borgatti, S.P. and Halgin, D. In press. “Analyzing Affiliation Networks”. In Carrington, P. and Scott, J. (eds) The Sage Handbook of Social Network Analysis. Sage Publications. http://www.steveborgatti.com/research/publications/bhaffiliations.pdf