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NetworkX 1.6
============
Release date: 20 November 2011
Highlights
~~~~~~~~~~
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.
New examples for drawing with D3 Javascript library, and ordering matrices with the Cuthill-McKee algorithm.
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.
Graph Classes
-------------
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
Isomorphisms
------------
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
Other
-----
* attracting_components
A list of lists is returned instead of a list of tuples.
* condensation
The condensation algorithm now takes a second argument (scc) and returns a
graph with nodes labeled as integers instead of node tuples.
* degree connectivity
average_in_degree_connectivity and average_out_degree_connectivity have
been replaced with
average_degree_connectivity(G, source='in', target='in')
and
average_degree_connectivity(G, source='out', target='out')
* neighbor degree
average_neighbor_in_degree and average_neighbor_out_degreey have
have been replaced with
average_neighbor_degree(G, source='in', target='in')
and
average_neighbor_degree(G, source='out', target='out')
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