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# scipy.sparse.csgraph.bellman_ford

`scipy.sparse.csgraph.``bellman_ford`(csgraph, directed=True, indices=None, return_predecessors=False, unweighted=False)

  Compute the shortest path lengths using the Bellman-Ford algorithm.

  The Bellman-ford algorithm can robustly deal with graphs with negative weights. If a negative cycle is detected, an error is raised. For graphs without negative edge weights, dijkstra’s algorithm may be faster.

  Parameters : | csgraph : array, matrix, or sparse matrix, 2 dimensions The N x N array of distances representing the input graph. directed : bool, optional If True (default), then find the shortest path on a directed graph: only move from point i to point j along paths csgraph[i, j]. If False, then find the shortest path on an undirected graph: the algorithm can progress from point i to j along csgraph[i, j] or csgraph[j, i] indices : array_like or int, optional if specified, only compute the paths for the points at the given indices. return_predecessors : bool, optional If True, return the size (N, N) predecesor matrix unweighted : bool, optional If True, then find unweighted distances. That is, rather than finding the path between each point such that the sum of weights is minimized, find the path such that the number of edges is minimized.
  Returns : | dist_matrix : ndarray The N x N matrix of distances between graph nodes. dist_matrix[i,j] gives the shortest distance from point i to point j along the graph. predecessors : ndarray Returned only if return_predecessors == True. The N x N matrix of predecessors, which can be used to reconstruct the shortest paths. Row i of the predecessor matrix contains information on the shortest paths from point i: each entry predecessors[i, j] gives the index of the previous node in the path from point i to point j. If no path exists between point i and j, then predecessors[i, j] = -9999
  Raises : | NegativeCycleError: : if there are negative cycles in the graph

  Notes

  This routine is specially designed for graphs with negative edge weights. If all edge weights are positive, then Dijkstra’s algorithm is a better choice.

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