py3plex.algorithms.community_detection.community package

Submodules

py3plex.algorithms.community_detection.community.community_louvain module

This module implements community detection.

class py3plex.algorithms.community_detection.community.community_louvain.Status

Bases: object

To handle several data in one struct.

Could be replaced by named tuple, but don’t want to depend on python 2.6

copy()

Perform a deep copy of status

degrees = {}
gdegrees = {}
init(graph, weight, part=None)

Initialize the status of a graph with every node in one community

internals = {}
node2com = {}
total_weight = 0
py3plex.algorithms.community_detection.community.community_louvain.best_partition(graph, partition=None, weight='weight', resolution=1.0, randomize=False)

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.

graphnetworkx.Graph

the networkx graph which is decomposed

partitiondict, optional

the algorithm will start using this partition of the nodes. It’s a dictionary where keys are their nodes and values the communities

weightstr, optional

the key in graph to use as weight. Default to ‘weight’

resolutiondouble, 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

randomizeboolean, optional

Will randomize the node evaluation order and the community evaluation order to get different partitions at each call

partitiondictionnary

The partition, with communities numbered from 0 to number of communities

NetworkXError

If the graph is not Eulerian.

generate_dendrogram to obtain all the decompositions levels

Uses Louvain algorithm

large networks. J. Stat. Mech 10008, 1-12(2008).

>>>  #Basic usage
>>> G=nx.erdos_renyi_graph(100, 0.01)
>>> part = best_partition(G)
>>> #other example to display a graph with its community :
>>> #better with karate_graph() as defined in networkx examples
>>> #erdos renyi don't have true community structure
>>> G = nx.erdos_renyi_graph(30, 0.05)
>>> #first compute the best partition
>>> partition = community.best_partition(G)
>>>  #drawing
>>> size = float(len(set(partition.values())))
>>> pos = nx.spring_layout(G)
>>> count = 0.
>>> for com in set(partition.values()) :
>>>     count += 1.
>>>     list_nodes = [nodes for nodes in partition.keys()
>>>                                 if partition[nodes] == com]
>>>     nx.draw_networkx_nodes(G, pos, list_nodes, node_size = 20,
                                node_color = str(count / size))
>>> nx.draw_networkx_edges(G, pos, alpha=0.5)
>>> plt.show()
py3plex.algorithms.community_detection.community.community_louvain.generate_dendrogram(graph, part_init=None, weight='weight', resolution=1.0, randomize=False)

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

graphnetworkx.Graph

the networkx graph which will be decomposed

part_initdict, optional

the algorithm will start using this partition of the nodes. It’s a dictionary where keys are their nodes and values the communities

weightstr, optional

the key in graph to use as weight. Default to ‘weight’

resolutiondouble, 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

dendrogramlist 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

TypeError

If the graph is not a networkx.Graph

best_partition

Uses Louvain algorithm

networks. J. Stat. Mech 10008, 1-12(2008).

>>> 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:
py3plex.algorithms.community_detection.community.community_louvain.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

partitiondict

a dictionary where keys are graph nodes and values the part the node belongs to

graphnetworkx.Graph

the initial graph

weightstr, optional

the key in graph to use as weight. Default to ‘weight’

gnetworkx.Graph

a networkx graph where nodes are the parts

>>> 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*(n-1)/2), (1, 1, n*(n-1)/2)])  # NOQA
>>> nx.is_isomorphic(int, goal)
True
py3plex.algorithms.community_detection.community.community_louvain.load_binary(data)

Load binary graph as used by the cpp implementation of this algorithm

py3plex.algorithms.community_detection.community.community_louvain.modularity(partition, graph, weight='weight')

Compute the modularity of a partition of a graph

partitiondict

the partition of the nodes, i.e a dictionary where keys are their nodes and values the communities

graphnetworkx.Graph

the networkx graph which is decomposed

weightstr, optional

the key in graph to use as weight. Default to ‘weight’

modularityfloat

The modularity

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

structure in networks. Physical Review E 69, 26113(2004).

>>> G=nx.erdos_renyi_graph(100, 0.01)
>>> part = best_partition(G)
>>> modularity(part, G)
py3plex.algorithms.community_detection.community.community_louvain.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

dendrogramlist of dict

a list of partitions, ie dictionnaries where keys of the i+1 are the values of the i.

levelint

the level which belongs to [0..len(dendrogram)-1]

partitiondictionnary

A dictionary where keys are the nodes and the values are the set it belongs to

KeyError

If the dendrogram is not well formed or the level is too high

best_partition which directly combines partition_at_level and generate_dendrogram to obtain the partition of highest modularity

>>> 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

py3plex.algorithms.community_detection.community.community_status module

class py3plex.algorithms.community_detection.community.community_status.Status

Bases: object

To handle several data in one struct.

Could be replaced by named tuple, but don’t want to depend on python 2.6

copy()

Perform a deep copy of status

degrees = {}
gdegrees = {}
init(graph, weight, part=None)

Initialize the status of a graph with every node in one community

internals = {}
node2com = {}
total_weight = 0

Module contents

This package implements community detection.

Package name is community but refer to python-louvain on pypi