Log Message:
Update py-networkx to 1.10, based on PR 50383 by Derouiche.

API changes

    [#1501] connected_components, weakly_connected_components, and \ 
strongly_connected_components return now a generator of sets of nodes. \ 
Previously the generator was of lists of nodes. This PR also refactored the \ 
connected_components and weakly_connected_components implementations making them \ 
faster, especially for large graphs.
    [#1547] The func_iter functions in Di/Multi/Graphs classes are slated for \ 
removal in NetworkX 2.0 release. func will behave like func_iter and return an \ 
iterator instead of list. These functions are deprecated in NetworkX 1.10 \ 
release.

New functionalities

    [#823] A enumerate_all_cliques function is added in the clique package \ 
(networkx.algorithms.clique) for enumerating all cliques (including nonmaximal \ 
ones) of undirected graphs.
    [#1105] A coloring package (networkx.algorithms.coloring) is created for \ 
graph coloring algorithms. Initially, a greedy_color function is provided for \ 
coloring graphs using various greedy heuristics.
    [#1193] 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.
    [#1194] A find_cycle function is added to the networkx.algorithms.cycles \ 
package 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.
    [#1241] A new networkx.algorithms.dominance package is added for \ 
dominance/dominator algorithms on directed graphs. It contains a \ 
immediate_dominators function for computing immediate dominators/dominator trees \ 
and a dominance_frontiers function 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.
    [#1280] The network simplex method in the networkx.algorithms.flow package \ 
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.
    [#1286] Added the Margulis–Gabber–Galil graph to networkx.generators.
    [#1306] 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, \ 
networkx.generators.expanders.
    [#1314] Allow overwriting of base class dict with dict-like: OrderedGraph, \ 
ThinGraph, LogGraph, etc.
    [#1321] Added to_pandas_dataframe and from_pandas_dataframe.
    [#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.
    [#1340] Added a from_pandas_dataframe function 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.
    [#1360] Added harmonic centrality to network.algorithms.centrality.
    [#1390] The generators.bipartite have 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.
    [#1413] Added transitive closure and antichains function for directed \ 
acyclic graphs in 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.
    [#1436] Added a generator function for circulant graphs to the \ 
networkx.generators.classic module.
    [#1437] Added function for computing quotient graphs; also created a new \ 
module, networkx.algorithms.minors.
    [#1438] Added longest_path and longest_path_length for DAG.
    [#1439] Added node and edge contraction functions to networkx.algorithms.minors.
    [#1445] Added a new modularity matrix module to networkx.linalg, and \ 
associated spectrum functions to the networkx.linalg.spectrum module.
    [#1447] Added function to generate all simple paths starting with the \ 
shortest ones based on Yen’s algorithm for finding k shortest paths at \ 
algorithms.simple_paths.
    [#1455] Added the directed modularity matrix to the \ 
networkx.linalg.modularity_matrix module.
    [#1474] Adds triadic_census function; also creates a new module, \ 
networkx.algorithms.triads.
    [#1476] 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 \ 
is_weighted, is_negatively_weighted, and is_empty.
    [#1481] Added Johnson’s algorithm; one more algorithm for shortest \ 
paths. It solves all pairs shortest path problem. This is johnson at \ 
algorithms.shortest_paths
    [#1414] Added Moody and White algorithm for identifying k_components in a \ 
graph, which is based on Kanevsky’s algorithm for finding all minimum-size \ 
node cut-sets (implemented in all_node_cuts #1391).
    [#1415] Added fast approximation for k_components to the \ 
networkx.approximation package. This is based on White and Newman approximation \ 
algorithm for finding node independent paths between two nodes (see #1405).

Removed functionalities

    [#1236] The legacy ford_fulkerson maximum flow function is removed. Use \ 
edmonds_karp instead.

Miscellaneous changes

    [#1192] Support for Python 2.6 is dropped.