Release date: 20 November 2011
New functions for finding articulation points, generating random bipartite graphs, constructing adjacency matrix representations, forming graph products, computing assortativity coefficients, measuring subgraph centrality and communicability, finding k-clique communities, and writing JSON format output.
More memory efficient implementation of current-flow betweenness and new approximation algorithms for current-flow betweenness and shortest-path betweenness.
Simplified handling of “weight” attributes for algorithms that use weights/costs/values.
Updated all code to work with the PyPy Python implementation http://pypy.org which produces faster performance on many algorithms.
The degree* methods in the graph classes (Graph, DiGraph, MultiGraph, MultiDiGraph) now take an optional weight= keyword that allows computing weighted degree with arbitrary (numerical) edge attributes. Setting weight=None is equivalent to the previous weighted=False.
Weighted graph algorithms¶
Many ‘weighted’ graph algorithms now take optional parameter to specify which edge attribute should be used for the weight (default=’weight’) (ticket https://networkx.lanl.gov/trac/ticket/573)
In some cases the parameter name was changed from weighted, to weight. Here is how to specify which edge attribute will be used in the algorithms:
Use weight=None to consider all weights equally (unweighted case)
Use weight=’weight’ to use the ‘weight’ edge attribute
Use weight=’other’ to use the ‘other’ edge attribute
Algorithms affected are:
to_scipy_sparse_matrix, clustering, average_clustering, bipartite.degree, spectral_layout, neighbor_degree, is_isomorphic, betweenness_centrality, betweenness_centrality_subset, vitality, load_centrality, mincost, shortest_path, shortest_path_length, average_shortest_path_length
Node and edge attributes are now more easily incorporated into isomorphism checks via the ‘node_match’ and ‘edge_match’ parameters. As part of this change, the following classes were removed:
WeightedGraphMatcher WeightedDiGraphMatcher WeightedMultiGraphMatcher WeightedMultiDiGraphMatcher
The function signature for ‘is_isomorphic’ is now simply:
is_isomorphic(g1, g2, node_match=None, edge_match=None)
See its docstring for more details. To aid in the creation of ‘node_match’ and ‘edge_match’ functions, users are encouraged to work with:
categorical_node_match categorical_edge_match categroical_multiedge_match numerical_node_match numerical_edge_match numerical_multiedge_match generic_node_match generic_edge_match generic_multiedge_match
These functions construct functions which can be passed to ‘is_isomorphic’. Finally, note that the above functions are not imported into the top-level namespace and should be accessed from ‘networkx.algorithms.isomorphism’. A useful import statement that will be repeated throughout documentation is:
import networkx.algorithms.isomorphism as iso
A list of lists is returned instead of a list of tuples.
The condensation algorithm now takes a second argument (scc) and returns a graph with nodes labeled as integers instead of node tuples.
average_in_degree_connectivity and average_out_degree_connectivity have been replaced with
average_degree_connectivity(G, source=’in’, target=’in’)
average_degree_connectivity(G, source=’out’, target=’out’)
average_neighbor_in_degree and average_neighbor_out_degreey have have been replaced with
average_neighbor_degree(G, source=’in’, target=’in’)
average_neighbor_degree(G, source=’out’, target=’out’)