# Analysis and applications Karger’s algorithm for Minimum Cut

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:

1. In war situation, a party would be interested in finding minimum number of links that break communication network of enemy.
2. The min-cut problem can be used to study reliability of a network (smallest number of edges that can fail).
3. Study of network optimization (find a maximum flow).
4. 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)
5. Matching problems (an NC algorithm for min-cut in directed graphs would result in an NC algorithm for maximum matching in bipartite graphs).
6. It can be used to find the minimum cut of a graph in polynomial time with a high probability of success.
7. The algorithm is particularly useful for graphs that are too large to be handled by other methods for finding the minimum cut.
8. Some common applications of Karger’s algorithm include image segmentation, network flow, and VLSI circuit design.
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