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Version 1.10 notes and API changes¶
This page includes more detailed release information and API changes from NetworkX 1.9 to NetworkX 1.10.
Please send comments and questions to the networkx-discuss mailing list: <http://groups.google.com/group/networkx-discuss>.
strongly_connected_componentsreturn now a generator of sets of nodes. Previously the generator was of lists of nodes. This PR also refactored the
weakly_connected_componentsimplementations making them faster, especially for large graphs.
func_iterfunctions in Di/Multi/Graphs classes are slated for removal in NetworkX 2.0 release.
funcwill behave like
func_iterand return an iterator instead of list. These functions are deprecated in NetworkX 1.10 release.
enumerate_all_cliquesfunction is added in the clique package (
networkx.algorithms.clique) for enumerating all cliques (including nonmaximal ones) of undirected graphs.
A coloring package (
networkx.algorithms.coloring) is created for graph coloring algorithms. Initially, a
greedy_colorfunction is provided for coloring graphs using various greedy heuristics.
A new generator
edge_dfs, added to
networkx.algorithms.traversal, implements a depth-first traversal of the edges in a graph. This complements functionality provided by a depth-first traversal of the nodes in a graph. For multigraphs, it allows the user to know precisely which edges were followed in a traversal. All NetworkX graph types are supported. A traversal can also reverse edge orientations or ignore them.
find_cyclefunction is added to the
networkx.algorithms.cyclespackage to find a cycle in a graph. Edge orientations can be optionally reversed or ignored.
- [#1210] Add a random generator for the duplication-divergence model.
networkx.algorithms.dominancepackage is added for dominance/dominator algorithms on directed graphs. It contains a
immediate_dominatorsfunction for computing immediate dominators/dominator trees and a
dominance_frontiersfunction for computing dominance frontiers.
- [#1269] The GML reader/parser and writer/generator are rewritten to remove the dependence on pyparsing and enable handling of arbitrary graph data.
The network simplex method in the
networkx.algorithms.flowpackage is rewritten to improve its performance and support multi- and disconnected networks. For some cases, the new implementation is two or three orders of magnitude faster than the old implementation.
Added the Margulis–Gabber–Galil graph to
Added the chordal p-cycle graph, a mildly explicit algebraic construction
of a family of 3-regular expander graphs. Also, moves both the existing
expander graph generator function (for the Margulis-Gabber-Galil
expander) and the new chordal cycle graph function to a new module,
- [#1314] Allow overwriting of base class dict with dict-like: OrderedGraph, ThinGraph, LogGraph, etc.
- [#1322] Added the Hopcroft–Karp algorithm for finding a maximum cardinality matching in bipartite graphs.
- [#1336] Expanded data keyword in G.edges and added default keyword.
- [#1338] Added support for finding optimum branchings and arborescences.
from_pandas_dataframefunction that accepts Pandas DataFrames and returns a new graph object. At a minimum, the DataFrame must have two columns, which define the nodes that make up an edge. However, the function can also process an arbitrary number of additional columns as edge attributes, such as ‘weight’.
- [#1354] Expanded layout functions to add flexibility for drawing subsets of nodes with distinct layouts and for centering each layout around given coordinates.
- [#1356] Added ordered variants of default graph class.
Added harmonic centrality to
generators.bipartitehave been moved to
algorithms.bipartite.generators. The functions are not imported in the main namespace, so to use it, the bipartite package has to be imported.
- [#1391] Added Kanevsky’s algorithm for finding all minimum-size separating node sets in an undirected graph. It is implemented as a generator of node cut sets.
- [#1399] Added power function for simple graphs
- [#1405] Added fast approximation for node connectivity based on White and Newman’s approximation algorithm for finding node independent paths between two nodes.
Added transitive closure and antichains function for directed acyclic
algorithms.dag. The antichains function was contributed by Peter Jipsen and Franco Saliola and originally developed for the SAGE project.
- [#1425] Added generator function for the complete multipartite graph.
- [#1427] Added nonisomorphic trees generator.
Added a generator function for circulant graphs to the
Added function for computing quotient graphs; also created a new module,
- [#1438] Added longest_path and longest_path_length for DAG.
Added node and edge contraction functions to
Added a new modularity matrix module to
networkx.linalg, and associated spectrum functions to the
Added function to generate all simple paths starting with the shortest
ones based on Yen’s algorithm for finding k shortest paths at
Added the directed modularity matrix to the
triadic_censusfunction; also creates a new module,
Adds functions for testing if a graph has weighted or negatively weighted
edges. Also adds a function for testing if a graph is empty. These are
Added Johnson’s algorithm; one more algorithm for shortest paths. It
solves all pairs shortest path problem. This is
Added Moody and White algorithm for identifying
k_componentsin a graph, which is based on Kanevsky’s algorithm for finding all minimum-size node cut-sets (implemented in
Added fast approximation for
networkx.approximationpackage. This is based on White and Newman approximation algorithm for finding node independent paths between two nodes (see #1405).
ford_fulkersonmaximum flow function is removed. Use