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# NetworkX 1.10

Release date: 2 August 2015

Support for Python 2.6 is dropped in this release.

## Highlights

- Connected components now return generators

- new functions including

  - enumerate_all_cliques, greedy_coloring, edge_dfs, find_cycle immediate_dominators, harmonic_centrality

  - Hopcraft–Karp algorithm for maximum matchings

  - optimum branchings and arborescences.

  - all_simple_paths

- pyparsing dependence removed from GML reader/parser

- improve flow algorithms

- new generators related to expander graphs.

- new generators for multipartite graphs, nonisomorphic trees, circulant graphs

- allow graph subclasses to use dict-like objects in place of dicts

- added ordered graph subclasses

- pandas dataframe read/write added.

- data keyword in G.edges() allows requesting edge attribute directly

- expanded layout flexibility for node subsets

- Kanesky’s algorithm for cut sets and k_components

- power function for graphs

- approximation of node connectivity

- transitive closure, triadic census and antichains

- quotient graphs and minors

- longest_path for DAGS

- modularity matrix routines

### 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, PrintGraph, 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.