py3plex.algorithms.community_detection.community package¶
Submodules¶
py3plex.algorithms.community_detection.community.community_louvain module¶
This module implements community detection.
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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
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copy
()¶ Perform a deep copy of status
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degrees
= {}¶
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gdegrees
= {}¶
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init
(graph, weight, part=None)¶ Initialize the status of a graph with every node in one community
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internals
= {}¶
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node2com
= {}¶
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total_weight
= 0¶
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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()
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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:
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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
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py3plex.algorithms.community_detection.community.community_louvain.
load_binary
(data)¶ Load binary graph as used by the cpp implementation of this algorithm
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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)
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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
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copy
()¶ Perform a deep copy of status
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degrees
= {}¶
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gdegrees
= {}¶
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init
(graph, weight, part=None)¶ Initialize the status of a graph with every node in one community
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internals
= {}¶
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node2com
= {}¶
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total_weight
= 0¶
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Module contents¶
This package implements community detection.
Package name is community but refer to python-louvain on pypi