How to detect redundant edge in graph networkx python?

Question

I have a question, how can I detect and get the redundant value from graph connection? i already set the redundant graph connection in add_edges_from() like (1,2) and (1,2). But when i print list of edges, it only detected as 1 connection. Here is my example:

test_graph = nx.Graph()
test_graph.add_edges_from([(1, 2), (1, 2), (1, 3), (1, 4), (3, 4), (4, 5), (5,6), (6,7), (7,8), (6,8)])

posxx = nx.spring_layout(test_graph, scale = 1, k = 2 / np.sqrt(test_graph.order())) #planar -> no intersect
nx.draw(test_graph, posxx, node_color='red', font_size = 10, node_size = 350, with_labels=True)

list(test_graph.edges)

Output :

[(1, 2), (1, 3), (1, 4), (3, 4), (4, 5), (5, 6), (6, 7), (6, 8), (7, 8)]

I want those redundant connection have output like integer number. Such as if i have (1,2),(1,2),(1,2), it is detected as 3 in edge value. Anyone have solution?

Paul Brodersen · Accepted Answer · 2022-10-28 10:12:28Z

2

Graphs with what you call "redundant" edges are typically called multigraphs. In networkx, you can either (1) use the MultiGraph class to keep track of all edges individually, or (2) you can count the edges and then create a weighted Graph.

#!/usr/bin/env python

import matplotlib.pyplot as plt
import networkx as nx

from collections import Counter

edges = [
    (1, 2), (1, 2),
    (1, 3),
    (1, 4),
    (3, 4),
    (4, 5),
    (5, 6),
    (6, 7),
    (6, 8),
    (7, 8),
]

# option 1: MultiGraph
g = nx.MultiGraph(edges)
print(g.edges)
 # MultiEdgeDataView([(1, 2), (1, 2), (1, 3), (1, 4), (3, 4), (4, 5), (5, 6), (6, 7), (6, 8), (7, 8)])

# option 2: weighted Graph
edge_to_count = Counter(edges)
weighted_edges = [(source, target, {'weight' : count}) for (source, target), count in edge_to_count.items()]
g = nx.Graph(weighted_edges)

print(g.edges())
# EdgeView([(1, 2), (1, 3), (1, 4), (3, 4), (4, 5), (5, 6), (6, 7), (6, 8), (7, 8)])

g.get_edge_data(1, 2)
# {'weight': 2}

answered Oct 28, 2022 at 10:12

Paul Brodersen

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1 Comment

Adi OS Over a year ago

Thank you so much for the answer, those weighted_edges part is the one i looking for my problem 🙏🏼

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