Using NetworkX, I have acquired a list of tuples describing edges (pairs of vertices) in a graph:
G = [(1, 2), (1, 3), (1, 4), (2, 4), (3, 8), (4, 5), (8, 15), (5, 6), (5, 7), (6, 17), (7, 11), (7, 15), (7, 16), (17, 12), (11, 12), (11, 13), (15, 9), (15, 10), (16, 9), (9, 18), (18, 13), (18, 14), (10, 14)]
Using this list, I want to loop over each vertex, and find each neighboring vertex, but I want to do this in order. So what I would like to get is for instance a nested list with the ith sublist containing each of the neighbors for vertex i.
Neighbors = [[2, 3, 4], [1, 4], [1, 8], [1, 2, 5], [4, 6, 7], [5, 17], [5, 11, 15, 16], [3, 15], [15, 16, 18], [14, 15], [7, 12, 13], [11, 17], [11, 18], [10, 18], [7, 8, 9, 10], [7, 9], [6, 12], [9, 13, 14]]
, but it could also be another sorted data structure.
However, since my graph could potentially contain a million edges and vertices, I want to achieve a routine that will not loop over the whole list for every vertex, since I want to keep the runtime low.
Are there any ways to achieve this? Any help is very much appreciated.
You can use a defaultdict as follows:
from collections import defaultdict
d = defaultdict(set)
for x, y in G:
d[x].add(y)
d[y].add(x)
d
defaultdict(set,
{1: {2, 3, 4},
2: {1, 4},
3: {1, 8},
4: {1, 2, 5},
5: {4, 6, 7},
6: {5, 17},
7: {5, 11, 15, 16},
8: {3, 15},
9: {15, 16, 18},
10: {14, 15},
11: {7, 12, 13},
12: {11, 17},
13: {11, 18},
14: {10, 18},
15: {7, 8, 9, 10},
16: {7, 9},
17: {6, 12},
18: {9, 13, 14}})
You can convert the dictionary to a list:
[sorted(d[k]) for k in range(1, max(d.keys())+1)]
[[2, 3, 4],
[1, 4],
[1, 8],
[1, 2, 5],
[4, 6, 7],
[5, 17],
[5, 11, 15, 16],
[3, 15],
[15, 16, 18],
[14, 15],
[7, 12, 13],
[11, 17],
[11, 18],
[10, 18],
[7, 8, 9, 10],
[7, 9],
[6, 12],
[9, 13, 14]]