Analysis and applications Karger’s algorithm for Minimum Cut
Last Updated :
27 Jan, 2023
We have introduced and discussed below Karger’s algorithm in set 1.
1) Initialize contracted graph CG as copy of original graph
2) While there are more than 2 vertices.
a) Pick a random edge (u, v) in the contracted graph.
b) Merge (or contract) u and v into a single vertex (update
the contracted graph).
c) Remove self-loops
3) Return cut represented by two vertices.
As discussed in the previous post, Karger’s algorithm doesn’t always find min cut. In this post, the probability of finding min-cut is discussed.
Probability that the cut produced by Karger’s Algorithm is Min-Cut is greater than or equal to 1/(n2)
Proof:
Let there be a unique Min-Cut of given graph and let there be C edges in the Min-Cut and the edges be {e1, e2, e3, .. ec}. The Karger’s algorithm would produce this Min-Cut if and only if none of the edges in set {e1, e2, e3, .. ec} is removed in iterations in the main while loop of above algorithm.
c is number of edges in min-cut
m is total number of edges
n is total number of vertices
S1 = Event that one of the edges in {e1, e2,
e3, .. ec} is chosen in 1st iteration.
S2 = Event that one of the edges in {e1, e2,
e3, .. ec} is chosen in 2nd iteration.
S3 = Event that one of the edges in {e1, e2,
e3, .. ec} is chosen in 3rd iteration.
..................
..................
The cut produced by Karger's algorithm would be a min-cut if none of the above
events happen.
So the required probability is P[S1' ∩ S2' ∩ S3' ∩ ............]
Probability that a min-cut edge is chosen in first iteration:
Let us calculate P[S1']
P[S1] = c/m
P[S1'] = (1 - c/m)
Above value is in terms of m (or edges), let us convert
it in terms of n (or vertices) using below 2 facts..
1) Since size of min-cut is c, degree of all vertices must be greater
than or equal to c.
2) As per Handshaking Lemma, sum of degrees of all vertices = 2m
From above two facts, we can conclude below.
n*c <= 2m
m >= nc/2
P[S1] <= c / (cn/2)
<= 2/n
P[S1] <= c / (cn/2)
<= 2/n
P[S1'] >= (1-2/n) ------------(1)
Probability that a min-cut edge is chosen in second iteration:
P[S1' ∩ S2'] = P[S2' | S1' ] * P[S1']
In the above expression, we know value of P[S1'] >= (1-2/n)
P[S2' | S1'] is conditional probability that is, a min cut is
not chosen in second iteration given that it is not chosen in first iteration
Since there are total (n-1) edges left now and number of cut edges is still c,
we can replace n by n-1 in inequality (1). So we get.
P[S2' | S1' ] >= (1 - 2/(n-1))
P[S1' ∩ S2'] >= (1-2/n) x (1-2/(n-1))
Probability that a min-cut edge is chosen in all iterations:
P[S1' ∩ S2' ∩ S3' ∩.......... ∩ Sn-2']
>= [1 - 2/n] * [1 - 2/(n-1)] * [1 - 2/(n-2)] * [1 - 2/(n-3)] *...
... * [1 - 2/(n - (n-4)] * [1 - 2/(n - (n-3)]
>= [(n-2)/n] * [(n-3)/(n-1)] * [(n-4)/(n-2)] * .... 2/4 * 2/3
>= 2/(n * (n-1))
>= 1/n2
How to increase probability of success?
The above probability of success of basic algorithm is very less. For example, for a graph with 10 nodes, the probability of finding the min-cut is greater than or equal to 1/100. The probability can be increased by repeated runs of basic algorithm and return minimum of all cuts found.
Applications:
- In war situation, a party would be interested in finding minimum number of links that break communication network of enemy.
- The min-cut problem can be used to study reliability of a network (smallest number of edges that can fail).
- Study of network optimization (find a maximum flow).
- Clustering problems (edges like associations rules) Matching problems (an NC algorithm for min-cut in directed graphs would result in an NC algorithm for maximum matching in bipartite graphs)
- Matching problems (an NC algorithm for min-cut in directed graphs would result in an NC algorithm for maximum matching in bipartite graphs).
- It can be used to find the minimum cut of a graph in polynomial time with a high probability of success.
- The algorithm is particularly useful for graphs that are too large to be handled by other methods for finding the minimum cut.
- Some common applications of Karger’s algorithm include image segmentation, network flow, and VLSI circuit design.
Like Article
Suggest improvement
Share your thoughts in the comments
Please Login to comment...