# Identify Articulation Points in an Undirected Graph (Solved)

## Introduction: Identify Articulation Points in an Undirected Graph (Solved)

Graphs are a versatile data structure that can be used to model various real-world problems, such as social networks, transportation systems, and even electrical circuits. In this article, we will discuss the concept of articulation points in an undirected graph and how to identify them using an algorithm. Articulation points, also known as cut vertices, are crucial nodes in a graph whose removal would lead to an increase in the number of connected components.

In this tutorial, we will cover:

- What are articulation points and why they are important?
- Real-world examples and scenarios of articulation points
- A real-world problem that involves finding articulation points
- Formal definition and problem statement
- A step-by-step solution to the problem
- Code examples in Python
- Explanation of the solution and its intuition
- Applying the solution to other real-world problems

## Understanding Articulation Points

An articulation point is a vertex (node) in an undirected graph whose removal, along with its associated edges, would increase the number of connected components. In simpler terms, removing an articulation point from a graph would break the graph into two or more disconnected parts. Identifying these critical points is crucial in many real-world applications, such as network design, transportation planning, and risk assessment.

## Real-World Examples and Scenarios

In a social network, articulation points can represent influential people whose removal would break the network into disconnected groups. Identifying these individuals can help businesses target their marketing efforts more effectively or assist in understanding the structure of social groups.

In transportation systems, articulation points can represent critical intersections or hubs that, if removed or damaged, would significantly impact the flow of traffic. Identifying these critical points can aid in disaster planning and help prioritize infrastructure investments.

In electrical circuits, articulation points can represent vulnerable components whose failure would lead to a loss of connectivity between other components. Identifying these points can improve the reliability and robustness of the circuit design.

## Real-World Problem: Analyzing a City's Road Network

Consider a city's road network represented as an undirected graph, where each intersection is a node and each road connecting two intersections is an edge. The city's transportation department wants to identify the critical intersections (articulation points) in the road network to plan for potential traffic disruptions if one of these intersections becomes inaccessible due to an accident or construction.

## Problem Statement and Formal Definition

Given an undirected graph `G = (V, E)`

, where `V`

is a set of nodes (intersections) and `E`

is a set of edges (roads), find all the articulation points in the graph.

### Input

- An undirected graph
`G`

represented by an adjacency list or matrix.

### Output

- A list of all articulation points in the graph.

## Tying the Problem Statement with the Real-World Scenario

In our city road network example, the input graph `G`

represents the city's road network, and the output list of articulation points corresponds to the critical intersections in the network.

## Solution: Tarjan's Algorithm for Finding Articulation Points

One efficient algorithm to identify articulation points in an undirected graph is Tarjan's Algorithm. This algorithm is based on Depth First Search (DFS) traversal and has a time complexity of O(V + E), where V is the number of vertices and E is the number of edges in the graph.

The algorithm uses three arrays:

`visited`

: To keep track of visited nodes during DFS traversal.`discovery_time`

: To store the discovery time (DFS start time) of each node.`low`

: To store the lowest discovery time reachable from a node through its subtree.

The algorithm also uses a `time`

variable to keep track of the current discovery time during the DFS traversal.

### Step-by-Step Solution with the City Road Network Example

Initialize the `visited`

, `discovery_time`

, and `low`

arrays to have a length equal to the number of nodes in the graph, and set all their values to -1.

Set the `time`

variable to 0.

Perform a DFS traversal on the graph, starting from an arbitrary node (e.g., node 0). During the traversal, for each node `u`

:

a. Mark `u`

as visited and set its `discovery_time[u]`

and `low[u]`

to the current value of `time`

.

b. Increment the `time`

variable.

c. For each neighbor `v`

of `u`

:

```
i. If `v` is not visited, set `v` as the parent of `u` and perform a DFS from `v`.
ii. After returning from the DFS call for `v`, update the `low[u]` value as the minimum of `low[u]` and `low[v]`.
iii. If `u` is not the root node and `low[v] >= discovery_time[u]`, then `u` is an articulation point.
iv. If `u` is the root node and has at least two children in the DFS tree, then `u` is an articulation point.
```

- After the DFS traversal is complete, all the articulation points in the graph will be identified.

## Code Example in Python

```
from collections import defaultdict
class Graph:
def __init__(self, vertices):
self.V = vertices
self.graph = defaultdict(list)
def add_edge(self, u, v):
self.graph[u].append(v)
self.graph[v].append(u)
def dfs(self, u, visited, discovery_time, low, parent, time, articulation_points):
visited[u] = True
discovery_time[u] = time
low[u] = time
children = 0
for v in self.graph[u]:
if not visited[v]:
parent[v] = u
children += 1
self.dfs(v, visited, discovery_time, low, parent, time + 1, articulation_points)
low[u] = min(low[u], low[v])
if parent[u] == -1 and children > 1:
articulation_points.add(u)
elif parent[u] != -1 and low[v] >= discovery_time[u]:
articulation_points.add(u)
elif v != parent[u]:
low[u] = min(low[u], discovery_time[v])
def find_articulation_points(self):
visited = [False] * self.V
discovery_time = [-1] * self.V
low = [-1] * self.V
parent = [-1] * self.V
time = 0
articulation_points = set()
for u in range(self.V):
if not visited[u]:
self.dfs(u, visited, discovery_time, low, parent, time, articulation_points)
return list(articulation_points)
```

## Explanation of the Solution and Intuition

Tarjan's Algorithm is based on the observation that a node `u`

is an articulation point if and only if:

`u`

is the root node of the DFS tree and has at least two children.`u`

is not the root node, and there is at least one child`v`

of`u`

such that no node in the subtree rooted at`v`

has a back edge to a node visited before`u`

.

The `low`

array is used to keep track of the back edges in the graph. Specifically, `low[u]`

stores the lowest discovery time reachable from the subtree rooted at node `u`

. If the `low`

value of a child `v`

of `u`

is greater than or equal to the discovery time of `u`

, it means that there is no back edge from `v`

's subtree to a node visited before `u`

, and thus `u`

is an articulation point.

## Applying the Solution to Other Real-World Problems

The algorithm for finding articulation points can be applied to various other real-world problems, such as:

- Identifying critical components in a computer network that, if removed, would cause a loss of connectivity between other components.
- Finding weak points in a water supply system where damage would lead to a loss of water supply to certain areas.
- Analyzing the structure of an organization to identify key personnel whose absence would significantly impact the organization's functioning.

By understanding and implementing algorithms such as Tarjan's Algorithm for finding articulation points, you can develop efficient solutions for a wide range of real-world challenges that involve the analysis of complex networks and systems.