Tarjan’s Algorithm: Strongly Connected Components

7 minute read

Tarjan’s algorithm1, 2 which runs in linear time is an algorithm in Graph Theory for finding the strongly connected components of a directed graph.

Bridges and Articulation Points

Bridges and Articulation Points are important in Graph Theory because in real-world situations, they often hint weak points, bottlenecks or vulnerabilities in the graph. Therefore, it is important to be able to quickly find and detect where and when they occur.


A Bridge (or cut-edge) in graph theory is any edge in a graph whose removal increases the number of connected components.

Bridge in Graph Theory

Lines with the red color are bridges because if you remove any of them, the graph is divided into two components.

Articulation Point

An Articulation Point (or cut-vertex) is any node in a graph whose removal increases the number of connected components.

Articulation Point in Graph Theory

Nodes marked with orange color are articulation points because if you remove any of them, graph is devided into two components.

Tarjan’s Algorithm

Tarjan’s Algorithm provides a very effective way to find these bridges and articulation points in linear time. We can explain this algorithm in 3 steps:

Step 1

Start at any node and do a Depth First Search (DFS) traversal, labeling nodes with an increasing id value as you go.

DFS Lebeling

Step 2

Keep track the id of each node and the smallest low link value.

Low Link Value of a node is defined as the smallest id reachable from that node when doing a Depth First Search (DFS), including itself.

Initially, all low link values can be initialized to the each node id.

Low Link Initial

If we inspect node 1 and node 2, we will notice that there exist a path going from node 1 and node 2 to node 0.

So, we should update both node 1 and node 2 low link values to 0.

Low Link 0-1-2

However, node 3, node 4 and node 5 are already at their optimal low link value because there are no other node they can reach with a smaller id.

Low Link 3-4-5

For node 6, node 7 and node 8, there is a path from node 6, node 7 and node 8 to node 5.

So, we should update node 6, node 7 and node 8 low link values to 5.

Low Link 6-7-8

Step 3

During the Depth First Search (DFS), bridges will be found where the id of node your edge is coming from is less than the low link value of the node your edge is going to.

Is Bridge?

Problem: Critical Connections in a Network

LeetCode 1192 - Critical Connections in a Network [hard]

There are n servers numbered from 0 to n-1 connected by undirected server-to-server connections forming a network where connections[i] = [a, b] represents a connection between servers a and b. Any server can reach any other server directly or indirectly through the network.

A critical connection is a connection that, if removed, will make some server unable to reach some other server.

Return all critical connections in the network in any order.

Example 1:

Critical Connections

Input: n = 4, connections = [[0, 1], [1, 2], [2, 0], [1, 3]]
Output: [[1, 3]]
Explanation: [[3, 1]] is also accepted.


  • 1 <= n <= 105
  • n-1 <= connections.length <= 105
  • connections[i][0] != connections[i][1]
  • There are no repeated connections.

Solution with using Tarjan’s Algorithm

from collections import defaultdict
from typing import List

class Solution:
    def criticalConnections(self, n: int, connections: List[List[int]]) -> List[List[int]]:
        def make_graph(connections):
            graph = defaultdict(list)
            for edge in connections:
                a, b = edge
            return graph

        graph = make_graph(connections)

        id, node, prev_node = 0, 0, -1  # at first there is no prev_node. we set it to -1
        ids = [0 for _ in range(n)]  # tracks ids of nodes
        low_links = [0 for _ in range(n)]  # tracks low link value (default value is the index)
        visited = [False for _ in range(n)]  # tracks DFS visit status

        bridges = []
        self.dfs(node, prev_node, bridges, graph, id, visited, ids, low_links)

        return bridges

    def dfs(self, node, prev_node, bridges, graph, id, visited, ids, low_links):
        visited[node] = True
        low_links[node] = id
        ids[node] = id
        id += 1

        for next_node in graph[node]:
            if next_node == prev_node:
            if not visited[next_node]:
                self.dfs(next_node, node, bridges, graph, id, visited, ids, low_links)
                low_links[node] = min(low_links[node], low_links[next_node])  # on callback, generates low link values
                if ids[node] < low_links[next_node]:  # found the bridge!
                    bridges.append([node, next_node])
                # tried to visit an already visited node, which may have a lower id than the current low link value
                low_links[node] = min(low_links[node], ids[next_node])

Time Complexity: O(E + V) –> One pass, linear time solution

Space Complexity: O(N)


  1. Wikipedia, Robert Tarjan
  2. Wikipedia, Tarjan’s strongly connected components algorithm
  3. YouTube, William Fiset - Bridges and Articulation points - Graph Theory