There are 'n' vertices and 0 edges of an undirected graph. What can be the maximum number of edges that we can draw such that the graph remains disconnected.
I have made the solution that we can exclude one vertex and can find the maximum number of edges between n-1 vertices of undirected graph, so that the graph still remains disconnected.
which is n(n-1)/2 for n vertices and will be (n-1)(n-2)/2 for n-1 vertices.
Can there be a better solution?
You can resolve this using analysis. Take your idea and generalize it. You divide the n vertices in two groups , of size x and n-x.
Now the number of edges is a function of x, expressed by
f(x)= x(x-1)/2 + (n-x)(n-x-1)/2
f(x) = 1/2(2x^2 - 2nx +n^2 - n)
The value which maximize this function is the partition size you want. If you make calculation you find that it decrease from x=0 to x=n/2, then increase to x=n. As x = 0 or x = n means the graph is collected, you take the next greatest value which is x=1. So your intuition is optimal.
Your solution should be the best solution.
Because any new edge added must have the nth vertex at one end.
If graph can have multi edges, answer is infinity for n>=3.
If it can also contain self-loops, answer is infinity for n>=2,
If none of those holds your solution is correct.
Related
An undirected graph having 'n' number of edges, then find out number of vertices that graph have?
Since an edge is a connection between to vertices, the amount of vertices is at max 2n.
The amount of vertices is at minimum n+1. (This is pretty logical if you imagine that you have 2 edges - then you will at minimum have 3 vertices, because each edge must connect 2 vertices)
So if e = n, then n+1 <= v <= 2n
There is no exact formula for the number of vertices in terms of number of edges in the general case. However, if you take special cases, you can say more: if the graph is a tree, then the number of vertices is one more than the number of edges.
I need help to solve this question :
you have undirected graph G=(V,E) you want to write an algorithm to adjust all
One of the edges, so that in the directed graph obtained, he number of incoming edges into the node be always greater than zero.
for all edge {u,v} ∈ E you should chose one direction (u,v) or (v,u).
When the answer is positive, the algorithm must return the intention of the edges - which fulfills the requirement
As was pointed out, this problem clearly does not always have a solution. Since every vertex must have at least one incoming edge, if we have E < V, the problem is impossible.
So, let's assume that we have E >= V. Here is one way to approach the algorithm: First, count the number of edges attached to each vertex in O(E) time. Note, my solution assumes appropriate storage like an adjacency list. Can you see why an adjacency matrix would have a worse complexity?
Second, we will make a binary minheap of the vertices, according to their corresponding edge count, in O(V). Some intuition: if we have a vertex with only one edge, we must convert that into an incoming edge! When we assign the direction of that edge, we need to update the edge count of the vertex on the other side. If that other vertex goes from 2 edges to 1, we now are forced to assign the direction of its one edge left. Visually:
1 - 2 - 1
Arbitrarily choose a 1 edge count to make directed
1 - 2 -> 0
2 just lost an edge, so update to 1!
1 - 1 -> 0
Since it only has 1 edge now, convert its edge to be incoming!
0 -> 0 -> 0
Obviously this graph doesn't work since V > E, but you get the idea.
So, V times, we extract the minimum from the heap and fix in O(logV). Each time, decrement the edge count of the neighbor. Assuming the adjacency list, we can find a neighbor (first element) and update the count in O(1), and we can fix the heap again in O(logV). Overall, this step takes O(V logV).
Note, if all of our remaining vertices have more than one possible edge, this approach will arbitrarily select one of the vertices with a smallest edge count and arbitrarily select one of its edges. I will let you think about why this works (or try to provide a counter-example if you think it doesn't!) Finally, if E > V, then when we finish, there may be extra unnecessary edges left over. In O(E) time, we can arbitrarily assign directions to those edges.
Overall, we are looking at V + V*logV + E aka O(E + VlogV). Hope this helps!
Given a DAG where are Edges have a Positive Edge Weight. Given a Value N.
Algorithm to calculate a simple (no cycles or node repetitions) Path with the Total weight N?
I am aware of the Algorithm where we have to find a Path of Given Path Length (number of Edges) but somewhat confused about for the Given Path Weight?
Can Dijkstra be modified for this case? Or anything else?
This is NP-complete, so don't expect any reasonably fast (polynomial-time) algorithm. Here's a reduction from the NP-complete Subset Sum problem, where we are given a multiset of n integers X = {x_1, x_2, ..., x_n} and a number k, and asked if there is any submultiset of the n numbers that sum to exactly k:
Create a graph G with n+1 vertices v_1, v_2, ..., v_{n+1}. For each vertex v_i, add edges to every higher-numbered vertex v_j, and give all these edges weight x_i. This graph has O(n^2) edges and can be constructed in O(n^2) time. Clearly it contains no cycles.
Suppose the answer to the Subset Sum problem is YES: That is, there exists a submultiset Y of X such that the numbers in Y total to exactly k. Actually, let Y = {y_1, y_2, ..., y_m} consist of the m <= n indices 1 <= i <= n of the selected elements of X. Then there is a corresponding path in the graph G with exactly the same weight -- namely the path that starts at v_{y_1}, takes the edge to v_{y_2} (which is of weight x_{y_1}), then takes the edge to v_{y_3}, and so on, finally arriving at v_{y_m} and taking a final edge (which is of weight x_{y_m}) to the terminal vertex v_{n+1}.
In the other direction, suppose that there is a simple path in G of total weight exactly k. Since the path is simple, each vertex appears at most once. Thus each edge in the path leaves a unique vertex. For each vertex v_i in the path except the last, add x_i to a set of chosen numbers: these numbers correspond to the edge weights in the path, so clearly they sum to exactly k, implying that the solution to the Subset Sum problem is also YES. (Notice that the position of the final vertex in the path doesn't matter, since we only care about the vertex that it leaves, and all edges leaving a vertex have the same weight.)
A YES answer to either problem implies a YES answer to the other problem, so a NO answer to either problem implies a NO answer to the other problem. Thus the answer to any Subset Sum problem instance can be found by first constructing the specified instance of your problem in polynomial time, and then using any algorithm for your problem to solve that instance -- so if an algorithm exists that can solve any instance of your problem in polynomial time, the NP-hard Subset Sum problem can also be solved in polynomial time.
Say I have some graph with nodes and undirected edges (the edges may have a weight associated to them).
I want to find all (or at least one) connected subgraphs that maximize the sum of the degree centrality of all nodes in the subgraph (the degree centrality is based on the original graph) under the constraint that the sum of the weighted edges is < X.
Is there an algorithm that will do this?
A quick search took me to this description of degree centrality. It turns out that the "degree centrality" of a vertex is simply its degree (neighbour count).
Unfortunately your problem is NP-hard, so it's very unlikely that any algorithm exists that can solve every instance quickly. First notice that, assuming edge weights are positive, the edges in any optimal solution necessarily form a tree, since in any non-tree you can delete at least 1 edge without destroying connectivity, and doing so will decrease the total edge weight of the subgraph. (So, as a positive spinoff: If you compute the minimum spanning tree of your input graph and find that it happens to have total weight < X, then you can simply include every vertex in the graph in your solution.)
Let's formulate a decision version of your problem. Given a graph G = (V, E) with positive (I'll assume) weights on the edges, a number X and a number Y, we want to know: Does there exist a connected subgraph G' = (V', E') of G such that the sum of the edge weights in E' is at most X, and the sum of the degrees of V' (w.r.t. G) is at least Y? (Clearly this is no harder than your original problem: If you had an algorithm to solve your problem, then you could just run it, add up the degrees of the vertices in the subgraph it found and compare this to Y to answer "my" problem.)
Here's a reduction from the NP-hard Steiner Tree in Graphs problem, where we are given a graph G = (V, E) with positive weights on the edges, a subset S of its vertices, and a number k, and the task is to determine whether it's possible to connect the vertices in S using a subset of edges with total weight at most k. (As I showed above, the solution will necessarily be a tree.) If the sum of all degrees in G is d, then all we need to do to transform G into an input for your problem is the following: For each vertex s_i in S we add enough new "ballast" vertices that are each connected only to s_i, via edges with weight X+1, to bring the degree of s_i up to d+1. We set X to k, and set Y to |S|(d+1).
Now suppose that the solution to the Steiner Tree problem is YES -- that is, there exists a subset of edges having total weight <= k that does connect all the vertices in S. In that case, it's clear that the same subgraph in the instance of your problem constructed above connects (possibly among others) all the vertices in S, and since each vertex in S has degree d+1, the total degree is at least |S|(d+1), so the answer to your decision problem is also YES.
In the other direction, suppose that the answer to your decision problem is YES -- that is, there exists a subset of edges having total weight <= X ( = k) that connects a set of vertices having total degree at least |S|(d+1). We need to show that this implies a YES answer to the original Steiner Tree problem. Clearly it suffices to show that the vertex set V' of any subgraph satisfying the conditions above (i.e. edges have total weight <= k and vertices have total degree >= |S|(d+1)) contains S (possibly among other vertices). So let V' be the vertex set of such a solution, and suppose to the contrary that there is some vertex u in S that is not in V'. But then the largest sum of degrees that we could possibly make would be to include all other non-ballast vertices in the graph in V', which would give a degree total of at most (|S|-1)(d+1) + d (the first term is the degree sum for the other vertices in S; the second is an upper bound on the degree sum of all non-S vertices in G; note that none of the ballast vertices we added in could be in the subgraph, because the only way to include any of them is to use an edge of weight X+1, which we obviously can't do). But clearly (|S|-1)(d+1) + d = |S|(d+1) - 1, which is strictly less than |S|(d+1), contradicting our assumption that V' has a degree total at least |S|(d+1). So it follows that S is a subset of V', and thus that it is possible to use the same subset of edges to connect the vertices in S for a total weight of at most k, i.e. that the answer to the Steiner Tree problem is also YES.
So a YES answer to either problem implies a YES answer to the other one, in turn implying that a NO answer to either implies a NO answer to the other. Thus if it were possible to solve the decision version of your problem in polynomial time, it would imply a polynomial-time solution to the NP-hard Steiner Tree in Graphs problem. This means the decision version of your problem is itself NP-hard, and so is the optimisation version (which as I said above is at least as hard). (The decision form is also NP-complete, since a YES answer can be easily verified in polynomial time.)
Sidenote: At first I thought I had a very straightforward reduction from the NP-hard Knapsack problem: Given a list of n weights w_1, ..., w_n and a list of n profits p_1, ..., p_n, make a single central vertex c, and n other vertices v_1, ..., v_n. For each v_i, attach it to c with an edge of weight w_i, and add p_i other leaf vertices, each attached only to v_i with an edge of weight X+1. However this reduction doesn't actually work, because the profits can be exponential in the input size n, meaning that the constructed instance of your problem might need to have an exponential number of vertices, which isn't allowed for a polynomial-time reduction.
I am trying to use bipartite graph to code my program with following properties:
in each side, there is N vertices
the graph is connected
Now, I wanna add a condition in my code which check if the number of edges is bigger than M, do not allow user to more activities(in a simple sentence print something in that condition) where M is maximum number edges such that it still has a unique maximum matching.
The question is how can I find M?
Any idea will be appreciated
Thanks
if you mean to find maximum m such that there is at least one graph with n vertices and m edges with a unique maximum matching, the answer is (n + 1) * n / 2.
to show that there is at least one graph with this number of edge, consider a graph with vertices x1, x2, .., xn in one part and vertices y1, .. yn in another part. draw an edge between vertex xi and yj iff (i <= j).
to show there can be no more edges, use induction on the number of vertices. first of all we can show if every vertex in the graph is connected to at least two vertex, the graph has at least two different maximum matching. (consider one maximum matching, follow a path from a vertex whose edges alternates between matching edges and non-matching edges, make a circle and reverse all the edges.)
so we know there is one vertex with degree equal to one. remove this vertex and it's neighbor and use induction on the remaining graph.
sorry for bad English.