# Articulation Points

Let's define what an *articulation point* is. We say that a vertex $$$V$$$ in a graph $$$G$$$ with $$$C$$$ connected components is an *articulation point* if its removal increases the number of connected components of $$$G$$$. In other words, let $$$C'$$$ be the number of connected components after removing vertex $$$V$$$, if $$$C' > C$$$ then $$$V$$$ is an *articulation point*.

## How to find articulation points?

## Naive approach O(V * (V + E))

```
For every vertex V in the graph G do
Remove V from G
if the number of connected components increases then V is an articulation point
Add V back to G
```

## Tarjan's approach O(V + E)

First, we need to know that an **ancestor** of some node $$$V$$$ is a node $$$A$$$ that was discoverd before $$$V$$$ in a DFS traversal.

In the graph $$$G_1$$$ shown above, if we start our DFS from **A** and follow the path to **C** through **B** ( **A** -> **B** -> **C** ), then **A** is an ancestor of **B** and **C** in this spanning tree generated from the DFS traversal.

#### Example of DFS spanning trees of a graph

Now that we know the definition of **ancestor** let's dive into the main idea.

### Idea

Let's say there is a node $$$V$$$ in some graph $$$G$$$ that can be reached by a node $$$U$$$ through some intermediate nodes (maybe non intermediate nodes) following some DFS traversal, if $$$V$$$ can also be reached by $$$A$$$ = "ancestor of $$$U$$$" without passing through $$$U$$$ then, $$$U$$$ is NOT an articulation point because it means that if we remove $$$U$$$ from $$$G$$$ we can still reach $$$V$$$ from $$$A$$$, hence, the number of *connected components* will remain the same.

So, we can conclude that the only 2 conditions for $$$U$$$ to be an *articulation point* are:

If all paths from $$$A$$$ to $$$V$$$ require $$$U$$$ to be in the graph.

If $$$U$$$ is the root of the DFS traversal with at least 2 children subgraphs disconnected from each other.

Then we can break condition #1 into 2 subconditions:

- $$$U$$$ is an
*articulation point*if it does not have an adyacent node $$$V$$$ that can reach $$$A$$$ without requiring $$$U$$$ to be in $$$G$$$.

- $$$U$$$ is an
*articulation point*if it is the root of some cycle in the DFS traversal.

### Examples:

Here **B** is an articulation point because all paths from ancestors of **B** to **C** require **B** to be in the graph.

Here **B** is NOT an articulation point because there is at least one path from an ancestor of **B** to **C** which does not require **B**.

Here **B** is an articulation point since it has at least 2 children subgraphs disconnected from each other.

### Implementation

Well, first thing we need is a way to know if some node $$$A$$$ is ancestor of some other node $$$V$$$, for this we can assign a *discovery time* to each vertex $$$V$$$ in the graph $$$G$$$ based on the DFS traversal, so that we can know which node was discovered before or after another. e.g. in $$$G_1$$$ with the traversal **A** -> **B** -> **C** the dicovery times for each node will be respectively 1, 2, 3; with this we know that **A** was discovered before **C** since `discovery_time[A] < discovery_time[C]`

.

Now we need to know if some vertex $$$U$$$ is an articulation point. So, for that we will check the following conditions:

If there is NO way to get to a node $$$V$$$ with

**strictly**smaller discovery time than the discovery time of $$$U$$$ following the DFS traversal, then $$$U$$$ is an articulation point. (it has to be**strictly**because if it is equal it means that $$$U$$$ is the root of a cycle in the DFS traversal which means that $$$U$$$ is still an*articulation point*).If $$$U$$$ is the root of the DFS tree and it has at least 2 children subgraphs disconnected from each other, then $$$U$$$ is an articulation point.

So, for implementation details, we will think of it as if for every node $$$U$$$ we have to find the node $$$V$$$ with the least discovery time that can be reached from $$$U$$$ following some DFS traversal path which does not require to pass **through** any already visited nodes, and let's call this node $$$low$$$.

Then, we can know that $$$U$$$ is an articulation point if the following condition is satisfied: `discovery_time[U] <= low[V]`

( $$$V$$$ in this case represents an adjacent node of $$$U$$$ ). To implement this, in each node $$$V$$$ we will store some identifier of its corresponding node $$$low$$$ found, this identifier will be the corresponding $$$low's$$$ discovery time because it helps us to know when the node $$$low$$$ was discovered, hence it helps us to know by which node we can discover $$$U$$$ first.

### C++ Code

```
// adj[u] = adjacent nodes of u
// ap = AP = articulation points
// p = parent
// disc[u] = discovery time of u
// low[u] = 'low' node of u
int dfsAP(int u, int p) {
int children = 0;
low[u] = disc[u] = ++Time;
for (int& v : adj[u]) {
if (v == p) continue; // we don't want to go back through the same path.
// if we go back is because we found another way back
if (!disc[v]) { // if V has not been discovered before
children++;
dfsAP(v, u); // recursive DFS call
if (disc[u] <= low[v]) // condition #1
ap[u] = 1;
low[u] = min(low[u], low[v]); // low[v] might be an ancestor of u
} else // if v was already discovered means that we found an ancestor
low[u] = min(low[u], disc[v]); // finds the ancestor with the least discovery time
}
return children;
}
void AP() {
ap = low = disc = vector<int>(adj.size());
Time = 0;
for (int u = 0; i < adj.size(); u++)
if (!disc[u])
ap[u] = dfsAP(u, u) > 1; // condition #2
}
```

## Bridges

Let's define what a *bridge* is. We say that an edge $$$UV$$$ in a graph $$$G$$$ with $$$C$$$ connected components is a *bridge* if its removal increases the number of connected components of $$$G$$$. In other words, let $$$C'$$$ be number of connected components after removing edge $$$UV$$$, if $$$C' > C$$$ then the edge $$$UV$$$ is a *bridge*.

The idea an implementation is exactly the same as for *articulation points* except for one thing, to say that the edge $$$UV$$$ is a bridge, the condition to satisfy is: `discovery_time[U] < low[V]`

instead of `discovery_time[U] <= low[V]`

. Notice that the only change was comparing strictly lesser instead of lesser of equal.

#### But why is this ?

If `discovery_time[U]`

is equal to `low[V]`

it means that there is a path from $$$V$$$ that goes back to $$$U$$$ ( $$$V$$$ in this case represents an adjacent node of $$$U$$$ ), or in other words we can just say that we found a cycle rooted in $$$U$$$. For *articulation points* if we remove $$$U$$$ from the graph it will increase the number of connected components, but in the case of *bridges* if we remove the edge $$$UV$$$ the number of connected components will remain the same. For *bridges* we need to be sure that the edge $$$UV$$$ is not involved in any cycle. A way to be sure of this is just to check that `low[V]`

is strictly greater than `discovery_time[U]`

.

In the graph shown above the edge $$$AB$$$ is a *bridge* because `low[B]`

is strictly greater than `disc[A]`

. The edge $$$BC$$$ is not a *bridge* because `low[C]`

is equal to `disc[B]`

.

### C++ Code

```
// br = bridges, p = parent
vector<pair<int, int>> br;
int dfsBR(int u, int p) {
low[u] = disc[u] = ++Time;
for (int& v : adj[u]) {
if (v == p) continue; // we don't want to go back through the same path.
// if we go back is because we found another way back
if (!disc[v]) { // if V has not been discovered before
dfsBR(v, u); // recursive DFS call
if (disc[u] < low[v]) // condition to find a bridge
br.push_back({u, v});
low[u] = min(low[u], low[v]); // low[v] might be an ancestor of u
} else // if v was already discovered means that we found an ancestor
low[u] = min(low[u], disc[v]); // finds the ancestor with the least discovery time
}
}
void BR() {
low = disc = vector<int>(adj.size());
Time = 0;
for (int u = 0; i < adj.size(); u++)
if (!disc[u])
dfsBR(u, u)
}
```

## FAQ

- Why
`low[u] = min(low[u], disc[v])`

instead of`low[u] = min(low[u], low[v])`

?

Let's consider node **C** in the graph above, in the DFS traversal the nodes after **C** are: **D** and **E**, when the DFS traversal reaches **E** we find **C** again, if we take its $$$low$$$ time, `low[E]`

will be equal to `disc[A]`

but at this point, when we return back to **C** in the DFS we will be omitting the fact that $$$U$$$ is the **root of a cycle** (which makes it an *articulation point*) and we will be saying that there is a path from **E** to some ancestor of **C** (in this case **A**) which does not require **C** and such path does not exist in the graph, therefore the algorithm will say that **C** is NOT an *articulation point* which is totally false since the only way to reach **D** and **E** is passing through **C**.

Good explanation, nice draws

$$$+\text{TREE}(\text{G}(64))$$$ $$$\text{prro :v}$$$ $$$\square$$$

The best explanation that I found, i've been looking for someone who could explain this subject, and I think I found that guy. +10 and to favorite.

Auto comment: topic has been updated by searleser97can you also explain how to find the biconnected components?

Sure, I will be elaborating on that on another post.

Good tutorial with nice explanations and examples

I think there is a little mistake