I need to implement an algorithm such that in a collection of unique and ordered graph edges, I can find a cyclic node.

E.g. for a ->b, b->c, c->a, then 'a' is a cyclic node and thus I want to annotate it in this edge with 'a@' and simillar for others.

As example data I use this:

a = [('a', 'b'), ('b', 'c'), ('c', 'd'), ('d', 'a'), ('a', 'e'), ('e', 'a'), ('f', 'e')]

Then this would become:

[('a', 'b'), ('b', 'c'), ('c', 'd'), ('d', 'a@'), ('a', 'e'), ('e', 'a@'), ('f', 'e')]

How can I achieve this in python?

This is what I tried:

collection = {}
data, result = [], []
for i, j in a:
if i in collection.keys():
collection[i].append(j)
else:
collection[i] = [j]
if j in collection.keys():
for item in range(len(collection[i])):
if collection[i][item] == j:
nr += 1
collection[i][item] = j + '@'
print(collection)

It seems to work for cycles but it also takes into account strong connected components that are not cycles.. So I am looking for something similar like networkx simple cycles (no subcycles), also I need data returned in this way like above.

This solution builds all possible paths that it encounters in the edge list, since we don't really know where a cycle starts. It also prunes the path list that it creates to prevent some memory bloat if your graph is large. It is ugly but works based on your needs.

a = [('a', 'b'), ('b', 'c'), ('c', 'd'), ('d', 'a'), ('a', 'e'), ('e', 'a'), ('f', 'e')]annotated = []
paths = []
for edge in a:new_paths = []paths.append(''.join(edge))annotated.append(edge)cycle = ''for path in paths[:]:if path.endswith(edge[0]):if path.startswith(edge[1]):annotated[-1] = (annotated[-1][0], annotated[-1][1]+'@')cycle = path + edge[1]else:new_paths.append(path + edge[1])else:new_paths.append(path)paths = [x for x in new_paths if x not in cycle]print(paths)
print(f'Result: {annotated}')"""
Out:['ab']
['abc', 'bc']
['abcd', 'bcd', 'cd']
[]
['ae']
[]
['fe']
Result: [('a', 'b'), ('b', 'c'), ('c', 'd'), ('d', 'a@'), ('a', 'e'), ('e', 'a@'), ('f', 'e')]
"""