Definition of cycle in undirected graph - graph

I can't find a formal definition of cycle in an undirected graph. The CLRS only reports a definition of symple cycles that i can't manage to generalize for a generic cycle.
This is the CLRS definition: a symple cycle in a undirected graph is a path <v0,v1,..,vk> so that:
k >= 3
v0 = vk
v1,..,vk are distincts
So i tried to remove the 3 condition to define a generic cycle but this can't work because we can have something like this: <a, b, c, b, a> that is obviously not a cycle.


I think you can generalize 3 as follows:
Either v1,...,vk are distinct or they contain a simple cycle (a contiguous list of vertices satisfying 1,2,&3).


Path in graph theory means list of edges or/and vertices satisfying some connectivity conditions. Cycle is closed path, first and last list element are same. Wikipedia article
Path or cycle is called simple if there are no repeated vertices or edges other than the starting and ending vertices. Your example is cycle, but not simple cycle.

Related

Time complexity for detecting a cycle in a graph

I am trying to understand the time complexity of some efficient methods of detecting cycles in a graph.
Two approaches for doing this are explained here. I would assume that time complexity is provided in terms of worst-case.
The first is union-find, which is said to have a time complexity of O(Vlog E).
The second uses a DFS based approach and is said to have a time complexity of O(V+E). If I am correct this is a more efficient complexity asymptotically than O(Vlog E). It is also convenient that the DFS-based approach can be used for directed and undirected graphs.
My issue is that I fail to see how the second approach can be considered to run in O(V+E) time because DFS runs in O(V+E) time and the algorithm checks the nodes adjacent to any discovered nodes for the starting node. Surely this would mean that the algorithm runs in O(V2) time because up to V-1 adjacent nodes might have to be traversed over for each discovered node? It is obviously impossible for more than one node to require the traversal of n-1 adjacent nodes but from my understanding this would still be the upper bound of the runtime.
Hopefully someone understands why I think this and can help me to understand why the complexity is O(V+E).

The algorithm, based on DFS, typically maintains a "visited" boolean variable for each vertex, which contains one bit of information - this vertex was already visited or not. So, none of vertices can be visited more than once.
If the graph is connected, then starting the DFS from any vertex will give you an answer right away. If the graph is a tree, then all the vertices will be visited in a single call to the DFS. If the graph is not a tree, then a single call to the DFS will find a cycle - and in this case not all the vertices might be visited. In both cases the subgraph, induced by all the already visited vertices, will be a tree at each step of the DFS lookup - so the total number of traversed edges will be O(V). Because of that we can reduce the time complexity estimate O(V+E) of the cycle detection algorithm to O(V).
Starting the DFS from all vertices of the graph is necessary in the case when the graph consists of a number of connected components - the "visited" boolean variable guarantees that the DFS won't traverse the same component again and again.

finding the total number of distinct shortest paths between 2 nodes in undirected weighted graph in linear time?

I was wondering, that if there is a weighted graph G(V,E), and I need to find a single shortest path between any two vertices S and T in it then I could have used the Dijkstras algorithm. but I am not sure how this can be done when we need to find all the distinct shortest paths from S to T. Is it solvable on O(n) time? I had one more question like if we assume that the weights of the edges in the graph can assume values only in certain range lets say 1 <=w(e)<=2 will this effect the time complexity?

You can do it using Dijkstra. In Dijkstra's algorithm, you assign labels to each node based on the distance it has from your source and settle nodes according to this distance (smallest first). Once the target node is settled, you have the length of the shortest path. To retrieve the path (=the sequence of edges), you can keep track of the parent of each node you settle. To retrieve all possible paths, you have to account for multiple parents of each node.
A small example:
Suppose you have a graph which looks like this (all edges weight 1 for simplicity):
B
/ \
A C - D
\ /
E
When you do dijkstra to find the distance A->D, you would get 3. You would first settle node A with distance 0, then nodes B and E with distance 1, node C with distance 2 and finally node D with distance 3. To get the paths, you would remember the parents of the nodes when you settle them. In this case you would first set the parents of B and C =node A. You then need to set the parents of node C to B and E as they both supply the same length. Node D would get node C as a parent.
When you need the paths, you could just follow the parent pointers, starting at D and branching each time a node has multiple parents. This would give you a branch at node C.
The post mentioned in the comment above uses the same principles, although it does not allow you to actually extract the paths, it only gives you the count.
One final remark: Dijkstra is not a linear time algorithm as you need to do a lot of operations to maintain you queue and node sets.

(a) Using BFS from s to mark nodes visited as usual, in the meantime, changing following things:
(b) for each node v, it has a record L(v) representing the layer that v is on and a record f(v)
representing the number of incoming paths:
(c) everytime a node v1 is found in another node's v2 neighbors:
(d) if v1 is not in the queue, add it into queue, L(v1) = L(v2) + 1,f(v1)+ = f(v2)
(d) if v1 is already in the queue and L(v1) equals L(v2), do nothing.
(e) if v1 is already in the queue but L(v1) doe not equal to L(v2), f(v1)+ = f(v2)
(f) until in this modied BFS, t pop from the queue for the rst time, we have f(t)
(g) and this f(t) represents the number of dierent shortest paths from s to t
This might solve your problem.

Find all BFS/DFS traversals

Given an undirected cyclic graph, I want to find all possible traversals with Breadth-First search or Depth-First search. That is given a graph as an adjacency-list:
A-BC
B-A
C-ADE
D-C
E-C
So all BFS paths from root A would be:
{ABCDE,ABCED,ACBDE,ACBED}
and for DFS:
{ABCDE,ABCED,ACDEB,ACEDB}
How would I generate those traversals algorithmically in a meaningful way? I suppose one could generate all permutations of letters and check their validity, but that seems like last-resort to me.
Any help would be appreciated.

Apart from the obvious way where you actually perform all possible DFS and BFS traversals you could try this approach:
Step 1.
In a dfs traversal starting from the root A transform the adjacency list of the currently visited node like so: First remove the parent of the node from the list. Second generate all permutations of the remaining nodes in the adj list.
So if you are at node C having come from node A you will do:
C -> ADE transform into C -> DE transform into C -> [DE, ED]
Step 2.
After step 1 you have the following transformed adj list:
A -> [CB, BC]
B -> []
C -> [DE, ED]
D -> []
E -> []
Now you launch a processing starting from (A,0), where the first item in the pair is the traversal path and the second is an index. Lets assume we have two queues. A BFS queue and a DFS queue. We put this pair into both queues.
Now we repeat the following, first for one queue until it is empty and then for the other queue.
We pop the first pair off the queue. We get (A,0). The node A maps to [BC, CB]. So we generate two new paths (ACB,1) and (ABC,1). Put these new paths in the queue.
Take the first one of these off the queue to get (ACB,1). The index is 1 so we look at the second character in the path string. This is C. Node C maps to [DE, ED].
The BFS children of this path would be (ACBDE,2) and (ACBED,2) which we obtained by appending the child permutation.
The DFS children of this path would be (ACDEB,2) and (ACEDB,2) which we obtained by inserting the child permutation right after C into the path string.
We generate the new paths according to which queue we are working on, based on the above and put them in the queue. So if we are working on the BFS queue we put in (ACBDE,2) and (ACBED,2). The contents of our queue are now : (ABC,1) , (ACBDE,2), (ACBED,2).
We pop (ABC,1) off the queue. Generate (ABC,2) since B has no children. And get the queue :
(ACBDE,2), (ACBED,2), (ABC,2) and so on. At some point we will end up with a bunch of pairs where the index is not contained in the path. For example if we get (ACBED,5) we know this is a finished path.

BFS is should be quite simple: each node has a certain depth at which it will be found. In your example you find A at depth 0, B and C at depth 1 and E and D at depth 2. In each BFS path, you will have the element with depth 0 (A) as the first element, followed by any permutation of the elements at depth 1 (B and C), followed by any permutation of the elements at depth 2 (E and D), etc...
If you look at your example, your 4 BFS paths match that pattern. A is always the first element, followed by BC or CB, followed by DE or ED. You can generalize this for graphs with nodes at deeper depths.
To find that, all you need is 1 Dijkstra search which is quite cheap.
In DFS, you don't have the nice separation by depth which makes BFS straightforward. I don't immediately see an algorithm that is as efficient as the one above. You could set up a graph structure and build up your paths by traversing your graph and backtracking. There are some cases in which this would not be very efficient but it might be enough for your application.

Find all possible paths from one vertex in a directed cyclic graph in Erlang

I would like to implement a function which finds all possible paths to all possible vertices from a source vertex V in a directed cyclic graph G.
The performance doesn't matter now, I just would like to understand the algorithm. I have read the definition of the Depth-first search algorithm, but I don't have full comprehension of what to do.
I don't have any completed piece of code to provide here, because I am not sure how to:
store the results (along with A->B->C-> we should also store A->B and A->B->C);
represent the graph (digraph? list of tuples?);
how many recursions to use (work with each adjacent vertex?).
How can I find all possible paths form one given source vertex in a directed cyclic graph in Erlang?
UPD: Based on the answers so far I have to redefine the graph definition: it is a non-acyclic graph. I know that if my recursive function hits a cycle it is an indefinite loop. To avoid that, I can just check if a current vertex is in the list of the resulting path - if yes, I stop traversing and return the path.
UPD2: Thanks for thought provoking comments! Yes, I need to find all simple paths that do not have loops from one source vertex to all the others.
In a graph like this:
with the source vertex A the algorithm should find the following paths:
A,B
A,B,C
A,B,C,D
A,D
A,D,C
A,D,C,B
The following code does the job, but it is unusable with graphs that have more that 20 vertices (I guess it is something wrong with recursion - takes too much memory, never ends):
dfs(Graph,Source) ->
?DBG("Started to traverse graph~n", []),
Neighbours = digraph:out_neighbours(Graph,Source),
?DBG("Entering recursion for source vertex ~w~n", [Source]),
dfs(Neighbours,[Source],[],Graph,Source),
ok.
dfs([],Paths,Result,_Graph,Source) ->
?DBG("There are no more neighbours left for vertex ~w~n", [Source]),
Result;
dfs([Neighbour|Other_neighbours],Paths,Result,Graph,Source) ->
?DBG("///The neighbour to check is ~w, other neighbours are: ~w~n",[Neighbour,Other_neighbours]),
?DBG("***Current result: ~w~n",[Result]),
New_result = relax_neighbours(Neighbour,Paths,Result,Graph,Source),
dfs(Other_neighbours,Paths,New_result,Graph,Source).
relax_neighbours(Neighbour,Paths,Result,Graph,Source) ->
case lists:member(Neighbour,Paths) of
false ->
?DBG("Found an unvisited neighbour ~w, path is: ~w~n",[Neighbour,Paths]),
Neighbours = digraph:out_neighbours(Graph,Neighbour),
?DBG("The neighbours of the unvisited vertex ~w are ~w, path is:
~w~n",[Neighbour,Neighbours,[Neighbour|Paths]]),
dfs(Neighbours,[Neighbour|Paths],Result,Graph,Source);
true ->
[Paths|Result]
end.
UPD3:
The problem is that the regular depth-first search algorithm will go one of the to paths first: (A,B,C,D) or (A,D,C,B) and will never go the second path.
In either case it will be the only path - for example, when the regular DFS backtracks from (A,B,C,D) it goes back up to A and checks if D (the second neighbour of A) is visited. And since the regular DFS maintains a global state for each vertex, D would have 'visited' state.
So, we have to introduce a recursion-dependent state - if we backtrack from (A,B,C,D) up to A, we should have (A,B,C,D) in the list of the results and we should have D marked as unvisited as at the very beginning of the algorithm.
I have tried to optimize the solution to tail-recursive one, but still the running time of the algorithm is unfeasible - it takes about 4 seconds to traverse a tiny graph of 16 vertices with 3 edges per vertex:
dfs(Graph,Source) ->
?DBG("Started to traverse graph~n", []),
Neighbours = digraph:out_neighbours(Graph,Source),
?DBG("Entering recursion for source vertex ~w~n", [Source]),
Result = ets:new(resulting_paths, [bag]),
Root = Source,
dfs(Neighbours,[Source],Result,Graph,Source,[],Root).
dfs([],Paths,Result,_Graph,Source,_,_) ->
?DBG("There are no more neighbours left for vertex ~w, paths are ~w, result is ~w~n", [Source,Paths,Result]),
Result;
dfs([Neighbour|Other_neighbours],Paths,Result,Graph,Source,Recursion_list,Root) ->
?DBG("~w *Current source is ~w~n",[Recursion_list,Source]),
?DBG("~w Checking neighbour _~w_ of _~w_, other neighbours are: ~w~n",[Recursion_list,Neighbour,Source,Other_neighbours]),
? DBG("~w Ready to check for visited: ~w~n",[Recursion_list,Neighbour]),
case lists:member(Neighbour,Paths) of
false ->
?DBG("~w Found an unvisited neighbour ~w, path is: ~w~n",[Recursion_list,Neighbour,Paths]),
New_paths = [Neighbour|Paths],
?DBG("~w Added neighbour to paths: ~w~n",[Recursion_list,New_paths]),
ets:insert(Result,{Root,Paths}),
Neighbours = digraph:out_neighbours(Graph,Neighbour),
?DBG("~w The neighbours of the unvisited vertex ~w are ~w, path is: ~w, recursion:~n",[Recursion_list,Neighbour,Neighbours,[Neighbour|Paths]]),
dfs(Neighbours,New_paths,Result,Graph,Neighbour,[[[]]|Recursion_list],Root);
true ->
?DBG("~w The neighbour ~w is: already visited, paths: ~w, backtracking to other neighbours:~n",[Recursion_list,Neighbour,Paths]),
ets:insert(Result,{Root,Paths})
end,
dfs(Other_neighbours,Paths,Result,Graph,Source,Recursion_list,Root).
Any ideas to run this in acceptable time?

Edit:
Okay I understand now, you want to find all simple paths from a vertex in a directed graph. So a depth-first search with backtracking would be suitable, as you have realised. The general idea is to go to a neighbour, then go to another one (not one which you've visited), and keep going until you hit a dead end. Then backtrack to the last vertex you were at and pick a different neighbour, etc.
You need to get the fiddly bits right, but it shouldn't be too hard. E.g. at every step you need to label the vertices 'explored' or 'unexplored' depending on whether you've already visited them before. The performance shouldn't be an issue, a properly implemented algorithm should take maybe O(n^2) time. So I don't know what you are doing wrong, perhaps you are visiting too many neighbours? E.g. maybe you are revisiting neighbours that you've already visited, and going round in loops or something.
I haven't really read your program, but the Wiki page on Depth-first Search has a short, simple pseudocode program which you can try to copy in your language. Store the graphs as Adjacency Lists to make it easier.
Edit:
Yes, sorry, you are right, the standard DFS search won't work as it stands, you need to adjust it slightly so that does revisit vertices it has visited before. So you are allowed to visit any vertices except the ones you have already stored in your current path.
This of course means my running time was completely wrong, the complexity of your algorithm will be through the roof. If the average complexity of your graph is d+1, then there will be approximately d*d*d*...*d = d^n possible paths.
So even if every vertex has only 3 neighbours, there's still quite a few paths when you get above 20 vertices.
There's no way around that really, because if you want your program to output all possible paths then indeed you will have to output all d^n of them.
I'm interested to know whether you need this for a specific task, or are just trying to program this out of interest. If the latter, you will just have to be happy with small, sparsely connected graphs.

I don't understand question. If I have graph G = (V, E) = ({A,B}, {(A,B),(B,A)}), there is infinite paths from A to B {[A,B], [A,B,A,B], [A,B,A,B,A,B], ...}. How I can find all possible paths to any vertex in cyclic graph?
Edit:
Did you even tried compute or guess growing of possible paths for some graphs? If you have fully connected graph you will get
2 - 1
3 - 4
4 - 15
5 - 64
6 - 325
7 - 1956
8 - 13699
9 - 109600
10 - 986409
11 - 9864100
12 - 108505111
13 - 1302061344
14 - 16926797485
15 - 236975164804
16 - 3554627472075
17 - 56874039553216
18 - 966858672404689
19 - 17403456103284420
20 - 330665665962403999
Are you sure you would like find all paths for all nodes? It means if you compute one milion paths in one second it would take 10750 years to compute all paths to all nodes in fully connected graph with 20 nodes. It is upper bound for your task so I think you don't would like do it. I think you want something else.

Not an improved algorithmic solution by any means, but you can often improve performance by spawning multiple worker threads, potentially here one for each first level node and then aggregating the results. This can often improve naive brute force algorithms relatively easily.
You can see an example here: Some Erlang Matrix Functions, in the maximise_assignment function (comments starting on line 191 as of today). Again, the underlying algorithm there is fairly naive and brute force, but the parallelisation speeds it up quite well for many forms of matrices.
I have used a similar approach in the past to find the number of Hamiltonian Paths in a graph.

Algorithms to Identify All the Cycle Bases in a UnDirected Graph

I have an undirected graph with Vertex V and Edge E. I am looking for an algorithm to identify all the cycle bases in that graph.
I think Tarjans algorithm is a good start. But the reference I have is about finding all of the cycles, not cycle base ( which, by definition is the cycle that cannot be constructed by union of other cycles).
For example, take a look at the below graph:
So, an algorithm would be helpful. If there is an existing implementation (preferably in C#), it's even better!

From what I can tell, not only is Brian's hunch spot on, but an even stronger proposition holds: each edge that's not in the minimum spanning tree adds exactly one new "base cycle".
To see this, let's see what happens when you add an edge E that's not in the MST. Let's do the favorite math way to complicate things and add some notation ;) Call the original graph G, the graph before adding E G', and the graph after adding E G''. So we need to find out how does the "base cycle count" change from G' to G''.
Adding E must close at least one cycle (otherwise E would be in the MST of G in the first place). So obviously it must add at least one "base cycle" to the already existing ones in G'. But does it add more than one?
It can't add more than two, since no edge can be a member of more than two base cycles. But if E is a member of two base cycles, then the "union" of these two base cycles must've been a base cycle in G', so again we get that the change in the number of cycles is still one.
Ergo, for each edge not in MST you get a new base cycle. So the "count" part is simple. Finding all the edges for each base cycle is a little trickier, but following the reasoning above, I think this could do it (in pseudo-Python):
for v in vertices[G]:
cycles[v] = []
for e in (edges[G] \ mst[G]):
cycle_to_split = intersect(cycles[e.node1], cycles[e.node2])
if cycle_to_split == None:
# we're adding a completely new cycle
path = find_path(e.node1, e.node2, mst[G])
for vertex on path:
cycles[vertex].append(path + [e])
cycles
else:
# we're splitting an existing base cycle
cycle1, cycle2 = split(cycle_to_split, e)
for vertex on cycle_to_split:
cycles[vertex].remove(cycle_to_split)
if vertex on cycle1:
cycles[vertex].append(cycle1)
if vertex on cycle2:
cycles[vertex].append(cycle2)
base_cycles = set(cycles)
Edit: the code should find all the base cycles in a graph (the base_cycles set at the bottom). The assumptions are that you know how to:
find the minimum spanning tree of a graph (mst[G])
find the difference between two lists (edges \ mst[G])
find an intersection of two lists
find the path between two vertices on a MST
split a cycle into two by adding an extra edge to it (the split function)
And it mainly follows the discussion above. For each edge not in the MST, you have two cases: either it brings a completely new base cycle, or it splits an existing one in two. To track which of the two is the case, we track all the base cycles that a vertex is a part of (using the cycles dictionary).

off the top of my head, I would start by looking at any Minimum Spanning Tree algorithm (Prim, Kruskal, etc). There can't be more base cycles (If I understand it correctly) than edges that are NOT in the MST....

The following is my actual untested C# code to find all these "base cycles":
public HashSet<List<EdgeT>> FindBaseCycles(ICollection<VertexT> connectedComponent)
{
Dictionary<VertexT, HashSet<List<EdgeT>>> cycles =
new Dictionary<VertexT, HashSet<List<EdgeT>>>();
// For each vertex, initialize the dictionary with empty sets of lists of
// edges
foreach (VertexT vertex in connectedComponent)
cycles.Add(vertex, new HashSet<List<EdgeT>>());
HashSet<EdgeT> spanningTree = FindSpanningTree(connectedComponent);
foreach (EdgeT edgeNotInMST in
GetIncidentEdges(connectedComponent).Except(spanningTree)) {
// Find one cycle to split, the HashSet resulted from the intersection
// operation will contain just one cycle
HashSet<List<EdgeT>> cycleToSplitSet =
cycles[(VertexT)edgeNotInMST.StartPoint]
.Intersect(cycles[(VertexT)edgeNotInMST.EndPoint]);
if (cycleToSplitSet.Count == 0) {
// Find the path between the current edge not in ST enpoints using
// the spanning tree itself
List<EdgeT> path =
FindPath(
(VertexT)edgeNotInMST.StartPoint,
(VertexT)edgeNotInMST.EndPoint,
spanningTree);
// The found path plus the current edge becomes a cycle
path.Add(edgeNotInMST);
foreach (VertexT vertexInCycle in VerticesInPathSet(path))
cycles[vertexInCycle].Add(path);
} else {
// Get the cycle to split from the set produced before
List<EdgeT> cycleToSplit = cycleToSplitSet.GetEnumerator().Current;
List<EdgeT> cycle1 = new List<EdgeT>();
List<EdgeT> cycle2 = new List<EdgeT>();
SplitCycle(cycleToSplit, edgeNotInMST, cycle1, cycle2);
// Remove the cycle that has been splitted from the vertices in the
// same cicle and add the results from the split operation to them
foreach (VertexT vertex in VerticesInPathSet(cycleToSplit)) {
cycles[vertex].Remove(cycleToSplit);
if (VerticesInPathSet(cycle1).Contains(vertex))
cycles[vertex].Add(cycle1);
if (VerticesInPathSet(cycle2).Contains(vertex))
cycles[vertex].Add(cycle2); ;
}
}
}
HashSet<List<EdgeT>> ret = new HashSet<List<EdgeT>>();
// Create the set of cycles, in each vertex should be remained only one
// incident cycle
foreach (HashSet<List<EdgeT>> remainingCycle in cycles.Values)
ret.AddAll(remainingCycle);
return ret;
}
Oggy's code was very good and clear but i'm pretty sure it contains an error, or it's me that don't understand your pseudo python code :)
cycles[v] = []
can't be a vertex indexed dictionary of lists of edges. In my opinion, it have to be a vertex indexed dictionary of sets of lists of edges.
And, to add a precisation:
for vertex on cycle_to_split:
cycle-to-split is probably an ordered list of edges so to iterate it through vertices you have to convert it in a set of vertices. Order here is negligible, so it's a very simple alghoritm.
I repeat, this is untested and uncomplete code, but is a step forward. It still requires a proper graph structure (i use an incidency list) and many graph alghoritms you can find in text books like Cormen. I wasn't able to find FindPath() and SplitCycle() in text books, and are very hard to code them in linear time of number of edges+vertices in the graph. Will report them here when I will test them.
Thanks a lot Oggy!

The standard way to detect a cycle is to use two iterators - for each iteration, one moves forward one step and the other two. Should there be a cycle, they will at some point point to each other.
This approach could be extended to record the cycles so found and move on.

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