igraph中兩個頂點之間的距離

Question

我有一個大（半百萬條邊）加權圖（非定向），我想找到兩個節點 u 和 v 之間的距離。我可以使用my_graph.shortest_paths(u, v, weights='length')來獲得距離。 但是，這真的很慢。

我也可以先找到路徑，然后計算它的長度。 這很快，但我不明白為什么這比直接計算長度要快。

在 networkx 我使用nx.shortest_path_length(my_graph u, v, weight='length')

我用這段代碼來計算速度。 對於任何想要運行代碼的人，我將edgelist放在 Google 驅動器上

import pandas as pd
import networkx as nx
import igraph
import time

# load edgelist
edgelist = pd.read_pickle('edgelist.pkl')

# create igraph
tuples = [tuple(x) for x in edgelist[['u', 'v', 'length']].values]
graph_igraph = igraph.Graph.TupleList(tuples, directed=False, edge_attrs=['length'])

# create nx graph
graph_nx = nx.from_pandas_edgelist(edgelist, source='u', target='v', edge_attr=True)


def distance_shortest_path(u, v):
    return graph_igraph.shortest_paths(u, v, weights='length')[0]

get_length = lambda edge: graph_igraph.es[edge]['length']
def distance_path_then_sum(u, v):
    path = graph_igraph.get_shortest_paths(u, v, weights='length', output='epath')[0]
    return sum(map(get_length, path))

def distance_nx(u, v):
    return nx.shortest_path_length(graph_nx, u, v, weight='length')


some_nodes = [
    'Delitzsch unt Bf',
    'Neustadt(Holst)Gbf',
    'Delitzsch ob Bf',
    'Karlshagen',
    'Berlin-Karlshorst (S)',
    'Köln/Bonn Flughafen',
    'Mannheim Hbf',
    'Neu-Edingen/Friedrichsfeld',
    'Ladenburg',
    'Heddesheim/Hirschberg',
    'Weinheim-Lützelsachsen',
    'Wünsdorf-Waldstadt',
    'Zossen',
    'Dabendorf',
    'Rangsdorf',
    'Dahlewitz',
    'Blankenfelde(Teltow-Fläming)',
    'Berlin-Schönefeld Flughafen',
    'Berlin Ostkreuz',
]

print('distance_shortest_path ', end='')
start = time.time()
for node in some_nodes:
    distance_shortest_path('Köln Hbf', node)
print('took', time.time() - start)

print('distance_nx ', end='')
start = time.time()
for node in some_nodes:
    distance_nx('Köln Hbf', node)
print('took', time.time() - start)

print('distance_path_then_sum ', end='')
start = time.time()
for node in some_nodes:
    distance_path_then_sum('Köln Hbf', node)
print('took', time.time() - start)

這導致

distance_shortest_path took 46.34037733078003
distance_nx took 12.006148099899292
distance_path_then_sum took 0.9555535316467285

Answer 1

您可以在igraph中使用shortest_paths function 。 使用非常簡單，假設G是您的圖，具有G.es['weight']邊權重，然后

D = G.shortest_paths(weights='weight'))

會給你一個igraph矩陣D 。 您可以將其轉換為numpy數組

D = np.array(list(D))

要僅獲取特定一對（組）節點之間的距離，您可以指定 shortest_paths 的source和target shortest_paths 。