Note
This documents the development version of NetworkX. Documentation for the current release can be found here.
networkx.algorithms.community.modularity_max.greedy_modularity_communities¶
-
greedy_modularity_communities(G, weight=None, resolution=1)[source]¶ Find communities in G using greedy modularity maximization.
This function uses Clauset-Newman-Moore greedy modularity maximization [2]. This method currently supports the Graph class.
Greedy modularity maximization begins with each node in its own community and joins the pair of communities that most increases modularity until no such pair exists.
This function maximizes the generalized modularity, where
resolutionis the resolution parameter, often expressed as \(\gamma\). Seemodularity().- Parameters
- GNetworkX graph
- weightstring or None, optional (default=None)
The name of an edge attribute that holds the numerical value used as a weight. If None, then each edge has weight 1. The degree is the sum of the edge weights adjacent to the node.
- Returns
- list
A list of sets of nodes, one for each community. Sorted by length with largest communities first.
See also
modularity
References
- 1
M. E. J Newman “Networks: An Introduction”, page 224 Oxford University Press 2011.
- 2
Clauset, A., Newman, M. E., & Moore, C. “Finding community structure in very large networks.” Physical Review E 70(6), 2004.
- 3
Reichardt and Bornholdt “Statistical Mechanics of Community Detection” Phys. Rev. E74, 2006.
Examples
>>> from networkx.algorithms.community import greedy_modularity_communities >>> G = nx.karate_club_graph() >>> c = list(greedy_modularity_communities(G)) >>> sorted(c[0]) [8, 14, 15, 18, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33]