algorithm - Find Longest Weighted Path from DAG with Networkx in Python? -


i need algorithm find longest weighted path in directed, acyclic graph networkx.multidigraph(). graph has weighted edges , many edges have null value weighting. in networkx doc have found nothing solve problem. graph has following structure:

>>> print graph.nodes() [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 16, 17, 20, 21, 22, 25, 26, 'end'] >>> print graph.edges() [(0, 'end'), (1, 0), (1, 10), (1, 5), (2, 1), (2, 11), (2, 6), (3, 2), (3, 12), (3, 7), (4, 8), (4, 3), (4, 13), (5, 'end'), (6, 5), (6, 15), (7, 16), (7, 6), (8, 17), (8, 7), (10, 'end'), (11, 10), (11, 20), (11, 15), (12, 16), (12, 11), (12, 21), (13, 17), (13, 12), (13, 22), (15, 'end'), (16, 25), (16, 15), (17, 16), (17, 26), (20, 'end'), (21, 25), (21, 20), (22, 26), (22, 21), (25, 'end'), (26, 25)] >>> print graph.edge[7][16] {1: {'weight': 100.0, 'time': 2}} >>> print graph.edge[7][6] {0: {'weight': 0, 'time': 2}}

i find her, have problems implementation:

  1. networkx: efficiently find absolute longest path in digraph solution without weightings.
  2. how find longest path python networkx? solution transformed weightings negativ values, graph has null values… , nx.dijkstra_path() not support negative values.

have idea or solution similar problem found?

take solution in link 1 , change line:

pairs = [[dist[v][0]+1,v] v in g.pred[node]] # incoming pairs

to like:

pairs = [[dist[v][0]+edge['weight'],v] u, edge in g[v][node] v in g.pred[node]] # incoming pairs

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