negative_edge_cycle(G, weight='weight', heuristic=True)
Edge weight attributes must be numerical. Distances are calculated as sums of weighted edges traversed.
This algorithm uses bellman_ford_predecessor_and_distance() but finds negative cycles on any component by first adding a new node connected to every node, and starting bellman_ford_predecessor_and_distance on that node. It then removes that extra node.
If this is a string, then edge weights will be accessed via the edge attribute with this key (that is, the weight of the edge joining u to v will be G.edges[u, v][weight]
). If no such edge attribute exists, the weight of the edge is assumed to be one.
If this is a function, the weight of an edge is the value returned by the function. The function must accept exactly three positional arguments: the two endpoints of an edge and the dictionary of edge attributes for that edge. The function must return a number.
Determines whether to use a heuristic to early detect negative cycles at a negligible cost. In case of graphs with a negative cycle, the performance of detection increases by at least an order of magnitude.
True if a negative edge cycle exists, otherwise False.
Returns True if there exists a negative edge cycle anywhere in G.
>>> G = nx.cycle_graph(5, create_using=nx.DiGraph())
... print(nx.negative_edge_cycle(G)) False
>>> G[1][2]["weight"] = -7See :
... print(nx.negative_edge_cycle(G)) True
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.shortest_paths.weighted.negative_edge_cycle
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