simple_cycles#
- simple_cycles(G, length_bound=None)[source]#
Find simple cycles (elementary circuits) of a graph.
A
simple cycle
, orelementary circuit
, is a closed path where no node appears twice. In a directed graph, two simple cycles are distinct if they are not cyclic permutations of each other. In an undirected graph, two simple cycles are distinct if they are not cyclic permutations of each other nor of the otherâ€™s reversal.Optionally, the cycles are bounded in length. In the unbounded case, we use a nonrecursive, iterator/generator version of Johnsonâ€™s algorithm [1]. In the bounded case, we use a version of the algorithm of Gupta and Suzumura[R155c03fc9e2e-2]_. There may be better algorithms for some cases [3] [4] [5].
The algorithms of Johnson, and Gupta and Suzumura, are enhanced by some well-known preprocessing techniques. When G is directed, we restrict our attention to strongly connected components of G, generate all simple cycles containing a certain node, remove that node, and further decompose the remainder into strongly connected components. When G is undirected, we restrict our attention to biconnected components, generate all simple cycles containing a particular edge, remove that edge, and further decompose the remainder into biconnected components.
Note that multigraphs are supported by this function â€“ and in undirected multigraphs, a pair of parallel edges is considered a cycle of length 2. Likewise, self-loops are considered to be cycles of length 1. We define cycles as sequences of nodes; so the presence of loops and parallel edges does not change the number of simple cycles in a graph.
- Parameters:
- GNetworkX DiGraph
A directed graph
- length_boundint or None, optional (default=None)
If length_bound is an int, generate all simple cycles of G with length at most length_bound. Otherwise, generate all simple cycles of G.
- Yields:
- list of nodes
Each cycle is represented by a list of nodes along the cycle.
- Raises:
- ValueError
when length_bound < 0.
See also
Notes
When length_bound is None, the time complexity is \(O((n+e)(c+1))\) for \(n\) nodes, \(e\) edges and \(c\) simple circuits. Otherwise, when length_bound > 1, the time complexity is \(O((c+n)(k-1)d^k)\) where \(d\) is the average degree of the nodes of G and \(k\) = length_bound.
References
[1]Finding all the elementary circuits of a directed graph. D. B. Johnson, SIAM Journal on Computing 4, no. 1, 77-84, 1975. https://doi.org/10.1137/0204007
[2]Finding All Bounded-Length Simple Cycles in a Directed Graph A. Gupta and T. Suzumura https://arxiv.org/abs/2105.10094
[3]Enumerating the cycles of a digraph: a new preprocessing strategy. G. Loizou and P. Thanish, Information Sciences, v. 27, 163-182, 1982.
[4]A search strategy for the elementary cycles of a directed graph. J.L. Szwarcfiter and P.E. Lauer, BIT NUMERICAL MATHEMATICS, v. 16, no. 2, 192-204, 1976.
[5]Optimal Listing of Cycles and st-Paths in Undirected Graphs R. Ferreira and R. Grossi and A. Marino and N. Pisanti and R. Rizzi and G. Sacomoto https://arxiv.org/abs/1205.2766
Examples
>>> edges = [(0, 0), (0, 1), (0, 2), (1, 2), (2, 0), (2, 1), (2, 2)] >>> G = nx.DiGraph(edges) >>> sorted(nx.simple_cycles(G)) [[0], [0, 1, 2], [0, 2], [1, 2], [2]]
To filter the cycles so that they donâ€™t include certain nodes or edges, copy your graph and eliminate those nodes or edges before calling. For example, to exclude self-loops from the above example:
>>> H = G.copy() >>> H.remove_edges_from(nx.selfloop_edges(G)) >>> sorted(nx.simple_cycles(H)) [[0, 1, 2], [0, 2], [1, 2]]