community API¶
This package implements community detection.
Package name is community but refer to pythonlouvain on pypi

community.
best_partition
(graph, partition=None, weight='weight', resolution=1.0, randomize=None, random_state=None)¶ Compute the partition of the graph nodes which maximises the modularity (or try..) using the Louvain heuristices
This is the partition of highest modularity, i.e. the highest partition of the dendrogram generated by the Louvain algorithm.
Parameters:  graph : networkx.Graph
the networkx graph which is decomposed
 partition : dict, optional
the algorithm will start using this partition of the nodes. It’s a dictionary where keys are their nodes and values the communities
 weight : str, optional
the key in graph to use as weight. Default to ‘weight’
 resolution : double, optional
Will change the size of the communities, default to 1. represents the time described in “Laplacian Dynamics and Multiscale Modular Structure in Networks”, R. Lambiotte, J.C. Delvenne, M. Barahona
 randomize : boolean, optional
Will randomize the node evaluation order and the community evaluation order to get different partitions at each call
 random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
Returns:  partition : dictionnary
The partition, with communities numbered from 0 to number of communities
Raises:  NetworkXError
If the graph is not Eulerian.
See also
generate_dendrogram
 to obtain all the decompositions levels
Notes
Uses Louvain algorithm
References
large networks. J. Stat. Mech 10008, 112(2008).
Examples
>>> # basic usage >>> import community as community_louvain >>> import networkx as nx >>> G = nx.erdos_renyi_graph(100, 0.01) >>> partion = community_louvain.best_partition(G)
>>> # display a graph with its communities: >>> # as ErdosRenyi graphs don't have true community structure, >>> # instead load the karate club graph >>> import community as community_louvain >>> import matplotlib.cm as cm >>> import matplotlib.pyplot as plt >>> import networkx as nx >>> G = nx.karate_club_graph() >>> # compute the best partition >>> partition = community_louvain.best_partition(G)
>>> # draw the graph >>> pos = nx.spring_layout(G) >>> # color the nodes according to their partition >>> cmap = cm.get_cmap('viridis', max(partition.values()) + 1) >>> nx.draw_networkx_nodes(G, pos, partition.keys(), node_size=40, >>> cmap=cmap, node_color=list(partition.values())) >>> nx.draw_networkx_edges(G, pos, alpha=0.5) >>> plt.show()

community.
generate_dendrogram
(graph, part_init=None, weight='weight', resolution=1.0, randomize=None, random_state=None)¶ Find communities in the graph and return the associated dendrogram
A dendrogram is a tree and each level is a partition of the graph nodes. Level 0 is the first partition, which contains the smallest communities, and the best is len(dendrogram)  1. The higher the level is, the bigger are the communities
Parameters:  graph : networkx.Graph
the networkx graph which will be decomposed
 part_init : dict, optional
the algorithm will start using this partition of the nodes. It’s a dictionary where keys are their nodes and values the communities
 weight : str, optional
the key in graph to use as weight. Default to ‘weight’
 resolution : double, optional
Will change the size of the communities, default to 1. represents the time described in “Laplacian Dynamics and Multiscale Modular Structure in Networks”, R. Lambiotte, J.C. Delvenne, M. Barahona
Returns:  dendrogram : list of dictionaries
a list of partitions, ie dictionnaries where keys of the i+1 are the values of the i. and where keys of the first are the nodes of graph
Raises:  TypeError
If the graph is not a networkx.Graph
See also
Notes
Uses Louvain algorithm
References
networks. J. Stat. Mech 10008, 112(2008).
Examples
>>> G=nx.erdos_renyi_graph(100, 0.01) >>> dendo = generate_dendrogram(G) >>> for level in range(len(dendo)  1) : >>> print("partition at level", level, >>> "is", partition_at_level(dendo, level)) :param weight: :type weight:

community.
induced_graph
(partition, graph, weight='weight')¶ Produce the graph where nodes are the communities
there is a link of weight w between communities if the sum of the weights of the links between their elements is w
Parameters:  partition : dict
a dictionary where keys are graph nodes and values the part the node belongs to
 graph : networkx.Graph
the initial graph
 weight : str, optional
the key in graph to use as weight. Default to ‘weight’
Returns:  g : networkx.Graph
a networkx graph where nodes are the parts
Examples
>>> n = 5 >>> g = nx.complete_graph(2*n) >>> part = dict([]) >>> for node in g.nodes() : >>> part[node] = node % 2 >>> ind = induced_graph(part, g) >>> goal = nx.Graph() >>> goal.add_weighted_edges_from([(0,1,n*n),(0,0,n*(n1)/2), (1, 1, n*(n1)/2)]) # NOQA >>> nx.is_isomorphic(ind, goal) True

community.
load_binary
(data)¶ Load binary graph as used by the cpp implementation of this algorithm

community.
modularity
(partition, graph, weight='weight')¶ Compute the modularity of a partition of a graph
Parameters:  partition : dict
the partition of the nodes, i.e a dictionary where keys are their nodes and values the communities
 graph : networkx.Graph
the networkx graph which is decomposed
 weight : str, optional
the key in graph to use as weight. Default to ‘weight’
Returns:  modularity : float
The modularity
Raises:  KeyError
If the partition is not a partition of all graph nodes
 ValueError
If the graph has no link
 TypeError
If graph is not a networkx.Graph
References
structure in networks. Physical Review E 69, 26113(2004).
Examples
>>> import community as community_louvain >>> import networkx as nx >>> G = nx.erdos_renyi_graph(100, 0.01) >>> partition = community_louvain.best_partition(G) >>> modularity(partition, G)

community.
partition_at_level
(dendrogram, level)¶ Return the partition of the nodes at the given level
A dendrogram is a tree and each level is a partition of the graph nodes. Level 0 is the first partition, which contains the smallest communities, and the best is len(dendrogram)  1. The higher the level is, the bigger are the communities
Parameters:  dendrogram : list of dict
a list of partitions, ie dictionnaries where keys of the i+1 are the values of the i.
 level : int
the level which belongs to [0..len(dendrogram)1]
Returns:  partition : dictionnary
A dictionary where keys are the nodes and the values are the set it belongs to
Raises:  KeyError
If the dendrogram is not well formed or the level is too high
See also
best_partition
 which directly combines partition_at_level and
generate_dendrogram
 to obtain the partition of highest modularity
Examples
>>> G=nx.erdos_renyi_graph(100, 0.01) >>> dendrogram = generate_dendrogram(G) >>> for level in range(len(dendrogram)  1) : >>> print("partition at level", level, "is", partition_at_level(dendrogram, level)) # NOQA