bellmanford 0.1.1

Small extensions of the Bellman-Ford routines in NetworkX, primarily for convenience (https://networkx.github.io).

This package provides a few small extensions of the Bellman-Ford routines in NetworkX, primarily for convenience.

Installation

bellmanford is available on PyPI:

pip install bellmanford

Usage

bellman_ford

length, nodes, negative_cycle = bellman_ford(G, source, target, weight='weight')

Compute shortest path and shortest path lengths between a source node and target node in weighted graphs using the Bellman-Ford algorithm.

Parameters

• G : NetworkX graph

• pred : dict - Keyed by node to predecessor in the path

• dist : dict - Keyed by node to the distance from the source

• source: node label - Source node

• target: node label - Target node

• weight : string - Edge data key corresponding to the edge weight

Returns

• length : numeric - Length of a negative cycle if one exists; otherwise length of a shortest path.

• nodes : list - Nodes in a negative edge cycle (in order) if one exists; otherwise nodes in a shortest path.

• negative_cycle : bool - True if a negative edge cycle exists, otherwise False.

Examples

>>> import networkx as nx
>>> G = nx.path_graph(5, create_using = nx.DiGraph())
>>> bf.bellman_ford(G, source=0, target=4)
(3, [1, 2, 3, 4], False)

negative_edge_cycle

length, nodes, negative_cycle = negative_edge_cycle(G, weight='weight')

If there is a negative edge cycle anywhere in G, returns True. Also returns the total weight of the cycle and the nodes in the cycle.

Parameters

• G : NetworkX graph

• weight : string, optional (default = 'weight') - Edge data key corresponding to the edge weight

Returns

• length : numeric - Length of a negative edge cycle if one exists, otherwise None.

• nodes : list - Nodes in a negative edge cycle (in order) if one exists, otherwise None.

• negative_cycle : bool - True if a negative edge cycle exists, otherwise False.

Examples

>>> import networkx as nx
>>> import bellmanford as bf
>>> G = nx.cycle_graph(5, create_using = nx.DiGraph())
>>> print(bf.negative_edge_cycle(G))
(None, [], False)
>>> G[1][2]['weight'] = -7
>>> print(bf.negative_edge_cycle(G))
(-3, [4, 0, 1, 2, 3, 4], True)