I would like to know if there is an algorithm which computes a minimum spanning tree (optimum branching) in a directed graph given a set of root vertices between all of these root vertices, but not only one root vertex and all other vertices in a graph.
Given a set of root vertices [1,4,6] and a graph G like the one on the following picture:
...the algorighm should return something like a green sub-graph on the same picture.
I would like to get such an MST that connects all the root vertices provided to the algorithm. I tend to think that the result of the would-be algorithm is a sub-graph of the graph G which contains all root vertices and some other vertices from G.
Notes:
I know that there is no MST for a directed graph, but there is Chu–Liu/Edmonds algorithm.
I guess that a result of such an algorithm (if it is actually possible) will return an optimum branching, which includes some vertices of a graph along with all root vertices.
Minimum Spanning Trees are supposed to span all the vertices. I think you might be actually dealing with a Steiner Tree problem, given that you only need to connect a subset of them. Unfortunately, the traditional Steiner tree problem with undirected edges is already NP complete so you have a tough road ahead of you.
Related
What's the difference between a Spanning Tree and a Spanning Forest in graphs, conceptually.
Also, is it possible to construct a Spanning Forest through DFS or BFS traversals? Why? How?
I understand the Spanning Tree, but I couldn't find any clear explanations about spanning forests. Even Wikipedia (https://en.wikipedia.org/wiki/Spanning_tree), doesn't give a clear definition about it.
My book (Data Structures & Algorithms, Wiley - sixth edition) also has no definition for spanning forests.
I was wondering, if we have a graph with for example three connected components in it, is it possible to construct a spanning forest by DFS/BFS traversals?
When there is only one connected component in your graph, the spanning tree = spanning forest.
But when there are multiple connected components in your graph. For example in following picture we have 3 connected components.:
So for each component, we will have a spanning tree, and all 3 spanning trees will constitute spanning forest
I was wondering, if we have a graph with for example three connected
components in it, is it possible to construct a spanning forest by
DFS/BFS traversals?
Yes it is possible. When there is only 1 connected component, your BFS or DFS will terminate visiting all the vertices and you will have a spanning tree (which in this case is equal to spanning forest).
But when you have more than 1 connected component, like in the picture, the only thing you have to do is start another BFS or DFS from an unvisited vertex. Your algorithm terminates when there is no unvisited vertex left and each BFS or DFS traversal will yield a spanning tree.
Non-trivial spanning forests can be constructed even for complete graphs via the following algorithm:
preconditions
all vertices are unmarked
the edges are enumerated from 1 to m
edge processing
if both of its adjacent vertices are marked, skip it because then that edge would either merge trees of the forest or creates a cycle in one of its trees
else mark its unmarked adjacent vertices
algorithm
process the edges in the order of their enumeration
explanaton:
while it is feasible in the construction of spanning trees to add edges that "bridge" connected components, those edges are not added in the above algorithm.
interpretation:
if the edges are enumerated according to ascending length, the edges of the resulting spanning forest will be a subsets of the MST and the trees of the forest will resemble "basins" i.e. the length of edges is smallest for the one that created the connected component and increases with every edge that is attached in later steps.
In that case the properties of spanning forest may provide insight into the structural properties of the original graph and/or be utilized in algorithms.
I currently studying about graphs and their algorithms, and i noticed a question which i don't know to to exactly prove:
If we have a connected, undirected graph G=(V,E), and every edge is with weight=1,is it true to say that every spanning tree that built from the shortest paths from the root, is a minimum spanning tree?
I ran some examples in http://visualgo.net/sssp.html and is seems for me that the answer for this question is true, but someone can show me how can i prove this?
and another question that crossed my mind, does the other direction is also true?
Every tree has exactly n - 1 edges. Since all weights are equal to 1, every spanning tree of G has a total weight of n - 1. It is also true for the minimal spanning tree. So the answer is yes.
TLDR: Not Necessarily
Consider the triangle graph with unit weights - it has three vertices x,y,z, and all three edges {x,y},{x,z},{y,z} have unit weight. The shortest path between any two vertices is the direct path. Agree?However, to satisfy the condition of a MST in the graph, you have to put together a set of 2 edges connecting the 3 vertices. Say {x,y}, {y,z} for example. This does not represent the shortest path between any pair of vertices.
Hence, your proposition is false :)
I would like to know of a fast algorithm to determine if a directed or undirected graph is a tree.
This post seems to deal with it, but it is not very clear; according to this link, if the graph is acyclic, then it is a tree. But if you consider the directed and undirected graphs below: in my opinion, only graphs 1 and 4 are trees. I suppose 3 is neither cyclic, nor a tree.
What needs to be checked to see if a directed or undirected graph is a tree or not, in an efficient way? And taking it one step ahead: if a tree exists then is it a binary tree or not?
For a directed graph:
Find the vertex with no incoming edges (if there is more than one or no such vertex, fail).
Do a breadth-first or depth-first search from that vertex. If you encounter an already visited vertex, it's not a tree.
If you're done and there are unexplored vertices, it's not a tree - the graph is not connected.
Otherwise, it's a tree.
To check for a binary tree, additionally check if each vertex has at most 2 outgoing edges.
For an undirected graph:
Check for a cycle with a simple depth-first search (starting from any vertex) - "If an unexplored edge leads to a node visited before, then the graph contains a cycle." If there's a cycle, it's not a tree.
If the above process leaves some vertices unexplored, it's not a tree, because it's not connected.
Otherwise, it's a tree.
To check for a binary tree, if the graph has more than one vertex, additionally check that all vertices have 1-3 edges (1 to the parent and 2 to the children).
Checking for the root, i.e. whether one vertex contains 1-2 edges, is not necessary as there has to be vertices with 1-2 edges in an acyclic connected undirected graph.
Note that identifying the root is not generically possible (it may be possible in special cases) as, in many undirected graphs, more than one of the nodes can be made the root if we were to make it a binary tree.
If an undirected given graph is a tree:
the graph is connected
the number of edges equals the number of nodes - 1.
An undirected graph is a tree when the following two conditions are true:
The graph is a connected graph.
The graph does not have a cycle.
A directed graph is a tree when the following three conditions are true:
The graph is a connected graph.
The graph does not have a cycle.
Each node except root should have exactly one parent.
Let G be an unweighted directed graph containing cycles. I'm looking for an algorithm which finds/creates all acyclic graphs G', composed of all vertices in G and a subset of edges of G, just small enough to make G' acyclic.
More formal: The desired algorithm consumes G and creates a set of acyclic graphs S, where each graph G' in S satisfies following properties:
G' contains all vertices of G.
G' contains a subset of edges of G, such that G' is acyclic.
The number of edges of G' is maximised. Which means: There is no G'' satisfying properties 1 and 2, such that G'' contains more edges then G' and G'' is acyclic.
Background: The original graph G models a pairwise ordering between elements. This can't be exploited as an ordering over all elements due to cycles in the graph. The maximal acyclic graphs G' therefore should model a best-possible approximation to this ordering, trying to respect as much of the pairwise ordering relation as possible.
In a naive approach, one could remove all possible combinations of edges and check for acyclicity after each removal. In this case there is a strongly branching tree of variations meaning bad time and space complexity.
Note: The problem may be related to a spanning tree, and you could define the G' graphs as a kind of directed spanning tree. But keep in mind that in my scenario a pair of edges in G' may have the same starting or the same ending vertex. This conflicts with some definitions of directed spanning trees used in literature.
EDIT: Added intuitive description, background information and note related to spanning trees.
This problem is called Feedback Arc Set. Since it is NP-hard, it is unlikely that you will find a scalable fast algorithm. However, if your instances are small, then algorithms such as the one from the paper “On enumerating all minimal solutions of feedback problems” by B. Schwikowski and E. Speckenmeyer might work.
Is there a graph algorithm that given a start(v) and an end(u) will find a shortest path through the given set of edges, but if u is a disconnected vertex, it will also determine the shortest path to add missing edges until u is no longer disconnected?
I have a pixel matrix where lines are made of 255's(black) and 0's(white). lines(255) can have breaks or spurs and I must get rid of both. I could have a pixel matrix forest with say 7 or so trees of black pixels. I need to find the true end points of each tree, find the single shortest path of each tree, then union all skew trees together to form 1 single line(ie, a single shortest path from the furthest 2 end points in the original matrix). all edge weights could be considered 1.
Thanks
How about running Dijkstra's algorithm and if disconnected, connect v and u? What's your criteria for "best place to add a missing edge?" Do edges have weights (like distance)?
Edit:
For one idea of "the best place," you could try the path that has minimal sum of shortest paths between all connected pairs. Floyd–Warshall algorithm can be used to find shortest paths between all pairs. So, run Floyd-Warshall for each node in v's tree and u.
Your problem isn't well defined for disconnected graphs. I can always add and edge between v and u.
If you meant that given an acyclic undirected disconnected graph, actually known as a forest, and given a subset of edges as a subgraph, can you find the shortest path between vertices, than this problem is trivial since if there is a path in the full graph, there is one path only.
If this is a general graph G, and you are talking about a forest subgraph G', than we need more info. Is this weighted? Is it only positive weights? If it is unweighted, do some variant of Dijksta. Define the diameter of a tree to be the length of the longest path between two leaves (this is well defined in a tree, since ther is only one such path). Let S be the sum of the diameters of all the trees in G', then set the weight all edges out of G' to 2S, then Dijkstra's algorithm will automatically prefer to step through G', stepping outside G' only when there is no choice, since walking through G' would always be cheaper.
Here you have following scenarios:
case:1. (all edges>0)
Negate all edges
find the longest path in graph using this:
https://www.geeksforgeeks.org/longest-path-undirected-tree/
negate the final result
case2: edges might be or might not be negative
Read Floyd–Warshall algorithm for this