Table of Contents

## 1. Definition

Given an undirected graph $G$ with $n$ vertices and $m$ edges. Both the edge connectivity and the vertex connectivity are characteristics describing the graph.

### 1.1. Edge connectivity

The **edge connectivity** $\lambda$ of the graph $G$ is the minimum number of edges that need to be deleted, such that the graph $G$ gets disconnected.

For example an already disconnected graph has an edge connectivity of $0$, a connected graph with at least one bridge has an edge connectivity of $1$, and a connected graph with no bridges has an edge connectivity of at least $2$.

We say that a set $S$ of edges **separates** the vertices $s$ and $t$, if, after removing all edges in $S$ from the graph $G$, the vertices $s$ and $t$ end up in different connected components.

It is clear, that the edge connectivity of a graph is equal to the minimum size of such a set separating two vertices $s$ and $t$, taken among all possible pairs $(s, t)$.

### 1.2. Vertex connectivity

The **vertex connectivity** $\kappa$ of the graph $G$ is the minimum number of vertices that need to be deleted, such that the graph $G$ gets disconnected.

For example an already disconnected graph has the vertex connectivity $0$, and a connected graph with an articulation point has the vertex connectivity $1$. We define that a complete graph has the vertex connectivity $n-1$. For all other graphs the vertex connectivity doesn’t exceed $n-2$, because you can find a pair of vertices which are not connected by an edge, and remove all other $n-2$ vertices.

We say that a set $T$ of vertices **separates** the vertices $s$ and $t$, if, after removing all vertices in $T$ from the graph $G$, the vertices end up in different connected components.

It is clear, that the vertex connectivity of a graph is equal to the minimal size of such a set separating two vertices $s$ and $t$, taken among all possible pairs $(s, t)$.

## 2. Properties

### 2.1. The Whitney inequalities

The **Whitney inequalities** (1932) gives a relation between the edge connectivity $\lambda$, the vertex connectivity $\kappa$ and the smallest degree of the vertices $\delta$: $$\kappa \le \lambda \le \delta$$

Intuitively if we have a set of edges of size $\lambda$, which make the graph disconnected, we can choose one of each end point, and create a set of vertices, that also disconnect the graph. And this set has size $\le \lambda$.

And if we pick the vertex and the minimal degree $\delta$, and remove all edges connected to it, then we also end up with a disconnected graph. Therefore the second inequality $\lambda \le \delta$.

It is interesting to note, that the Whitney inequalities cannot be improved: i.e. for any triple of numbers satisfying this inequality there exists at least one corresponding graph. One such graph can be constructed in the following way: The graph will consists of $2(\delta + 1)$ vertices, the first $\delta + 1$ vertices form a clique (all pairs of vertices are connected via an edge), and the second $\delta + 1$ vertices form a second clique. In addition we connect the two cliques with $\lambda$ edges, such that it uses $\lambda$ different vertices in the first clique, and only $\kappa$ vertices in the second clique. The resulting graph will have the three characteristics.

### 2.2. The Ford-Fulkerson theorem

The **Ford-Fulkerson theorem** implies, that the biggest number of edge-disjoint paths connecting two vertices, is equal to the smallest number of edges separating these vertices.

## 3. Computing the values

### 3.1. Edge connectivity using maximum flow

This method is based on the Ford-Fulkerson theorem.

We iterate over all pairs of vertices $(s, t)$ and between each pair we find the largest number of disjoint paths between them. This value can be found using a maximum flow algorithm: we use $s$ as the source, $t$ as the sink, and assign each edge a capacity of $1$. Then the maximum flow is the number of disjoint paths.

The complexity for the algorithm using Edmonds-Karp is $O(V^2 V E^2) = O(V^3 E^2)$. But we should note, that this includes a hidden factor, since it is practically impossible to create a graph such that the maximum flow algorithm will be slow for all sources and sinks. Especially the algorithm will run pretty fast for random graphs.

### 3.2. Special algorithm for edge connectivity

The task of finding the edge connectivity if equal to the task of finding the **global minimum cut**.

Special algorithms have been developed for this task. One of them is the Stoer-Wagner algorithm, which works in $O(V^3)$ or $O(V E)$ time.

### 3.3. Vertex connectivity

Again we iterate over all pairs of vertices $s$ and $t$, and for each pair we find the minimum number of vertices that separates $s$ and $t$.

By doing this, we can apply the same maximum flow approach as described in the previous sections.

We split each vertex $x$ with $x \neq s$ and $x \neq t$ into two vertices $x_1$ and $x_2$. We connect these to vertices with a directed edge $(x_1, x_2)$ with the capacity $1$, and replace all edges $(u, v)$ by the two directed edges $(u_2, v_1)$ and $(v_2, u_1)$, both with the capacity of 1. The by the construction the value of the maximum flow will be equal to the minimum number of vertices that are needed to separate $s$ and $t$.

This approach has the same complexity as the flow approach for finding the edge connectivity.