Karger's algorithm

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A graph and two of its cuts. The dotted line in red is a cut with three crossing edges. The dashed line in green is a min-cut of this graph, crossing only two edges.

In computer science and graph theory, Karger's algorithm is a randomized algorithm to compute a minimum cut of a connected graph. It was invented by David Karger and first published in 1993.[1]

The idea of the algorithm is based on the concept of contraction of an edge (u, v) in an undirected graph G = (V, E). Informally speaking, the contraction of an edge merges the nodes u and v into one, reducing the total number of nodes of the graph by one. All other edges connecting either u or v are "reattached" to the merged node, effectively producing a multigraph. Karger's basic algorithm iteratively contracts randomly chosen edges until only two nodes remain; those nodes represent a cut in the original graph. By iterating this basic algorithm a sufficient number of times, a minimum cut can be found with high probability.

The global minimum cut problem[edit]

Main article: Minimum cut

A cut (S,T) in an undirected graph G = (V, E) is a partition of the vertices V into two non-empty, disjoint sets S\cup T= V. The cutset of a cut consists of the edges \{\, uv \in E \colon u\in S, v\in T\,\} between the two parts. The size (or weight) of a cut in an unweighted graph is the cardinality of the cutset, i.e., the number of edges between the two parts,

w(S,T) = |\{\, uv \in E \colon u\in S, v\in T\,\}|\,.

There are 2^{|V|} ways of choosing for each vertex whether it belongs to S or to T, but two of these choices make S or T empty and do not give rise to cuts. Among the remaining choices, swapping the roles of S and T does not change the cut, so each cut is counted twice; therefore, there are 2^{|V|-1}-1 distinct cuts. The minimum cut problem is to find a cut of smallest size among these cuts.

For weighted graphs with positive edge weights w\colon E\rightarrow \mathbf R^+ the weight of the cut is the sum of the weights of edges between vertices in each part

w(S,T) = \sum_{uv\in E\colon u\in S, v\in T} w(uv)\,,

which agrees with the unweighted definition for w=1.

A cut is sometimes called a “global cut” to distinguish it from an “s-t cut” for a given pair of vertices, which has the additional requirement that s\in S and t\in T. Every global cut is an s-t cut for some s,t\in V. Thus, the minimum cut problem can be solved in polynomial time by iterating over all choices of s,t\in V and solving the resulting minimum s-t cut problem using the max-flow min-cut theorem and a polynomial time algorithm for maximum flow, such as the push-relabel algorithm, though this approach is not optimal. Better deterministic algorithms for the global minimum cut problem include the Stoer–Wagner algorithm, which has a running time of O(mn+n^2\log n).[2]

Contraction algorithm[edit]

The fundamental operation of Karger’s algorithm is a form of edge contraction. The result of contracting the edge e=\{u,v\} is new node uv. Every edge \{w,u\} or \{w,v\} for w\notin\{u,v\} to the endpoints of the contracted edge is replaced by an edge \{w,uv\} to the new node. Finally, the contracted nodes u and v with all their incident edges are removed. In particular, the resulting graph contains no self-loops. The result of contracting edge e is denoted G/e.

The marked edge is contracted into a single node.

The contraction algorithm repeatedly contracts random edges in the graph, until only two nodes remain, at which point there is only a single cut.

Successful run of Karger’s algorithm on a 10-vertex graph. The minimum cut has size 3.
   procedure contract(G=(V,E)):
   while |V| > 2
       choose e\in E uniformly at random
       G \leftarrow G/e
   return the only cut in G

When the graph is represented using adjacency lists or an adjacency matrix, a single edge contraction operation can be implemented with a linear number of updates to the data structure, for a total running time of O(|V|^2). Alternatively, the procedure can be viewed as an execution of Kruskal’s algorithm for constructing the minimum spanning tree in a graph where the edges have weights w(e_i)=\pi(i) according to a random permutation \pi. Removing the heaviest edge of this tree results in two components that describe a cut. In this way, the contraction procedure can be implemented like Kruskal’s algorithm in time O(|E|\log |E|).

The random edge choices in Karger’s algorithm correspond to an execution of Kruskal’s algorithm on a graph with random edge ranks until only two components remain.

The best known implementations use O(|E|) time and space, or O(|E|\log |E|) time and O(|V|) space, respectively.[1]

Success probability of the contraction algorithm[edit]

In a graph G=(V,E) with n=|V| vertices, the contraction algorithm returns a minimum cut with polynomially small probability \binom{n}{2}^{-1}. Every graph has 2^{n-1} -1 cuts,[3] among which at most \tbinom{n}{2} can be minimum cuts. Therefore, the success probability for this algorithm is much better than the probability for picking a cut at random, which is at most \tbinom{n}{2}/( 2^{n-1} -1 )

For instance, the cycle graph on n vertices has exactly \binom{n}{2} minimum cuts, given by every choice of 2 edges. The contraction procedure finds each of these with equal probability.

To establish the bound on the success probability in general, let C denote the edges of a specific minimum cut of size k. The contraction algorithm returns C if none of the random edges belongs to the cutset of C. In particular, the first edge contraction avoids C, which happens with probability 1-k/|E|. The minimum degree of G is at least k (otherwise a minimum degree vertex would induce a smaller cut), so |E|\geq nk/2. Thus, the probability that the contraction algorithm picks an edge from C is

\frac{k}{|E|} \leq \frac{k}{nk/2} = \frac{2}{n}.

The probability p_n that the contraction algorithm on an n-vertex graph avoids C satisfies the recurrence p_n \geq \bigl(1- \frac{2}{n}\bigr) p_{n-1}, with p_2 = 1, which can be expanded as

p_n \geq \prod_{i=0}^{n-3} \Bigl(1-\frac{2}{n-i}\Bigr) =
 \prod_{i=0}^{n-3} {\frac{n-i-2}{n-i}}
      = \frac{n-2}{n}\cdot \frac{n-3}{n-1} \cdot \frac{n-4}{n-2}\cdots \frac{3}{5}\cdot \frac{2}{4} \cdot \frac{1}{3}
      = \binom{n}{2}^{-1}\,.

Repeating the contraction algorithm[edit]

10 repetitions of the contraction procedure. The 5th repetition finds the minimum cut of size 3.

By repeating the contraction algorithm  T = \binom{n}{2}\ln n times with independent random choices and returning the smallest cut, the probability of not finding a minimum cut is

      \leq \frac{1}{e^{\ln n}} = \frac{1}{n}\,.

The total running time for T repetitions for a graph with n vertices and m edges is  O(Tm) = O(n^2 m \log n).

Karger–Stein algorithm[edit]

An extension of Karger’s algorithm due to David Karger and Clifford Stein achieves an order of magnitude improvement.[4]

The basic idea is to perform the contraction procedure until the graph reaches t vertices.

   procedure contract(G=(V,E), t):
   while |V| > t
       choose e\in E uniformly at random
       G \leftarrow G/e
   return G

The probability p_{n,t} that this contraction procedure avoids a specific cut C in an n-vertex graph is

p_{n,t} \ge \prod_{i=0}^{n-t-1} \Bigl(1-\frac{2}{n-i}\Bigr) = \binom{t}{2}\Bigg/\binom{n}{2}\,.

This expression is \Omega(t^2/n^2) and becomes less than \frac{1}{2} around  t= \lceil 1 + n/\sqrt 2\rceil. In particular, the probability that an edge from C is contracted grows towards the end. This motivates the idea of switching to a slower algorithm after a certain number of contraction steps.

   procedure fastmincut(G= (V,E)):
   if |V| \le 6:
       return mincut(V)
       t\leftarrow \lceil 1 + |V|/\sqrt 2\rceil
       G_1 \leftarrow  contract(G, t)
       G_2 \leftarrow  contract(G, t)
       return min {fastmincut(G_1), fastmincut(G_2)}


The probability P(n) the algorithm finds a specific cutset C is given by the recurrence relation

P(n)= 1-\left(1-\frac{1}{2} P\left(\Bigl\lceil 1 + \frac{n}{\sqrt{2}}\Bigr\rceil \right)\right)^2

with solution P(n) = O\left(\frac{1}{\log n}\right). The running time of fastmincut satisfies

T(n)= 2T\left(\Bigl\lceil 1+\frac{n}{\sqrt{2}}\Bigr\rceil\right)+O(n^2)

with solution T(n)=O(n^2\log n). To achieve error probability O(1/n), the algorithm can be repeated O(\log n/P(n)) times, for an overall running time of T(n) \cdot \frac{\log n}{P(n)} = O(n^2\log ^3 n). This is an order of magnitude improvement over Karger’s original algorithm.

Finding all min-cuts[edit]

Theorem: With high probability we can find all min cuts in the running time of O(n^2\ln ^3 n).

Proof: Since we know that P(n) = O\left(\frac{1}{\ln n}\right), therefore after running this algorithm O(\ln ^2 n) times The probability of missing a specific min-cut is

\Pr[\text{miss a specific min-cut}] = (1-P(n))^{O(\ln ^2 n)} \le \left(1-\frac{c}{\ln n}\right)^{\frac{3\ln ^2 n}{c}} \le e^{-3\ln n} = \frac{1}{n^3}.

And there are at most \binom{n}{2} min-cuts, hence the probability of missing any min-cut is

\Pr[\text{miss any min-cut}] \le \binom{n}{2} \cdot \frac{1}{n^3} = O\left(\frac{1}{n}\right).

The probability of failures is considerably small when n is large enough.∎

Improvement bound[edit]

To determine a min-cut, one has to touch every edge in the graph at least once, which is O(n^2) time in a dense graph. The Karger–Stein's min-cut algorithm takes the running time of O(n^2\ln ^{O(1)} n), which is very close to that.


  1. ^ a b Karger, David (1993). "Global Min-cuts in RNC and Other Ramifications of a Simple Mincut Algorithm". Proc. 4th Annual ACM-SIAM Symposium on Discrete Algorithms. 
  2. ^ Stoer, M.; Wagner, F. (1997). "A simple min-cut algorithm". Journal of the ACM 44 (4): 585. doi:10.1145/263867.263872. 
  3. ^ Patrignani, Maurizio; Pizzonia, Maurizio (2001), "The complexity of the matching-cut problem", in Brandstädt, Andreas; Le, Van Bang, Graph-Theoretic Concepts in Computer Science: 27th International Workshop, WG 2001, Boltenhagen, Germany, June 14ÔÇô16, 2001, Proceedings, Lecture Notes in Computer Science 2204, Berlin: Springer, pp. 284–295, doi:10.1007/3-540-45477-2_26, MR 1905640 .
  4. ^ Karger, David R.; Stein, Clifford (1996). "A new approach to the minimum cut problem" (PDF). Journal of the ACM 43 (4): 601. doi:10.1145/234533.234534.