Bridges in Graph
Algorithm · Stretch
Step through it: Depth-First Search pattern
start at 7
Step 1 of 6
Goal: visit every node. Depth-first search picks a direction and goes as deep as it can before turning back, like exploring a maze by always taking the next corridor.
Step 2 of 6
From the root, 7, go down the left side to 9.
Step 3 of 6
Keep going down to 5. It has no children: a dead end.
Step 4 of 6
Back up to 9 and try its other child, 2. Another dead end.
Step 5 of 6
Back up to the root and take the last branch: 11. Recursion remembers where to return to (a stack does the same). On a graph also keep a visited list so you never loop. This is depth-first search.
Step 6 of 6
Your turn.
In graph theory, a bridge, isthmus, cut-edge, or cut arc is an edge of a graph whose deletion increases its number of connected components. Equivalently, an edge is a bridge if and only if it is not contained in any cycle. A graph is said to be bridgeless or isthmus-free if it contains no bridges.
A graph with 16 vertices and 6 bridges (highlighted in red)
An undirected connected graph with no cut edges
Code
import depthFirstSearch from '../depth-first-search/depthFirstSearch';
/**
* Helper class for visited vertex metadata.
*/
class VisitMetadata {
constructor({ discoveryTime, lowDiscoveryTime }) {
this.discoveryTime = discoveryTime;
this.lowDiscoveryTime = lowDiscoveryTime;
}
}
/**
* @param {Graph} graph
* @return {Object}
*/
export default function graphBridges(graph) {
// Set of vertices we've already visited during DFS.
const visitedSet = {};
// Set of bridges.
const bridges = {};
// Time needed to discover to the current vertex.
let discoveryTime = 0;
// Peek the start vertex for DFS traversal.
const startVertex = graph.getAllVertices()[0];
const dfsCallbacks = {
/**
* @param {GraphVertex} currentVertex
*/
enterVertex: ({ currentVertex }) => {
// Tick discovery time.
discoveryTime += 1;
// Put current vertex to visited set.
visitedSet[currentVertex.getKey()] = new VisitMetadata({
discoveryTime,
lowDiscoveryTime: discoveryTime,
});
},
/**
* @param {GraphVertex} currentVertex
* @param {GraphVertex} previousVertex
*/
leaveVertex: ({ currentVertex, previousVertex }) => {
if (previousVertex === null) {
// Don't do anything for the root vertex if it is already current (not previous one).
return;
}
// Check if current node is connected to any early node other then previous one.
visitedSet[currentVertex.getKey()].lowDiscoveryTime = currentVertex.getNeighbors()
.filter((earlyNeighbor) => earlyNeighbor.getKey() !== previousVertex.getKey())
.reduce(
/**
* @param {number} lowestDiscoveryTime
* @param {GraphVertex} neighbor
*/
(lowestDiscoveryTime, neighbor) => {
const neighborLowTime = visitedSet[neighbor.getKey()].lowDiscoveryTime;
return neighborLowTime < lowestDiscoveryTime ? neighborLowTime : lowestDiscoveryTime;
},
visitedSet[currentVertex.getKey()].lowDiscoveryTime,
);
// Compare low discovery times. In case if current low discovery time is less than the one
// in previous vertex then update previous vertex low time.
const currentLowDiscoveryTime = visitedSet[currentVertex.getKey()].lowDiscoveryTime;
const previousLowDiscoveryTime = visitedSet[previousVertex.getKey()].lowDiscoveryTime;
if (currentLowDiscoveryTime < previousLowDiscoveryTime) {
visitedSet[previousVertex.getKey()].lowDiscoveryTime = currentLowDiscoveryTime;
}
// Compare current vertex low discovery time with parent discovery time. Check if there
// are any short path (back edge) exists. If we can't get to current vertex other then
// via parent then the parent vertex is articulation point for current one.
const parentDiscoveryTime = visitedSet[previousVertex.getKey()].discoveryTime;
if (parentDiscoveryTime < currentLowDiscoveryTime) {
const bridge = graph.findEdge(previousVertex, currentVertex);
bridges[bridge.getKey()] = bridge;
}
},
allowTraversal: ({ nextVertex }) => {
return !visitedSet[nextVertex.getKey()];
},
};
// Do Depth First Search traversal over submitted graph.
depthFirstSearch(graph, startVertex, dfsCallbacks);
return bridges;
}References
Practice
Depth-First Search
- Maximum Depth of Binary TreeEasy
- Path SumEasy
- Diameter of Binary TreeEasy
- Balanced Binary TreeEasy
- Find if Path Exists in GraphEasy
- Path Sum IIMedium
- Path Sum IIIMedium
- Clone GraphMedium
- Number of ProvincesMedium
- All Paths From Source to TargetMedium
- Course ScheduleMedium
- Pacific Atlantic Water FlowMedium
- Count Good Nodes in Binary TreeMedium
- Sum Root to Leaf NumbersMedium
- Reconstruct ItineraryMedium
- Is Graph Bipartite?Medium
- Binary Tree Maximum Path SumHard
- Longest Increasing Path in a MatrixHard
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