## Can we find the shortest path in unweighted graph?

We say that BFS is the algorithm to use if we want to find the shortest path in an undirected, unweighted graph. The claim for BFS is that the first time a node is discovered during the traversal, that distance from the source would give us the shortest path. The same cannot be said for a weighted graph.

## Does Dijkstra work for unweighted graphs?

If there are no negative weight cycles, then we can solve in O(E + VLogV) time using Dijkstra’s algorithm. Since the graph is unweighted, we can solve this problem in O(V + E) time.

**Which of the following algorithms find the shortest path in an unweighted graph?**

Problem: Given an unweighted undirected graph, we have to find the shortest path from the given source to the given destination using the Breadth-First Search algorithm.

**How do you find the shortest path in a matrix?**

The idea is to BFS (breadth first search) on matrix cells. Note that we can always use BFS to find shortest path if graph is unweighted. Store each cell as a node with their row, column values and distance from source cell. Start BFS with source cell.

### What is unweighted shortest path algorithm?

Unweighted graph: breadth-first search Breadth-first search is a method for traversing a tree or graph data structure. As a convenient side effect, it automatically computes the shortest path between a source node and each of the other nodes in the tree or graph.

### Is Dijkstra better than BFS?

If you consider travel websites, these use Dijkstra’s algorithm because of weights (distances) on nodes. If you will consider the same distance between all nodes, then BFS is the better choice.

**Can BFS find shortest path in weighted graph?**

The shortest path between two vertices is defined to be the path whose sum of edge weights is the least. BFS will not work on weighted graphs since the path with the fewest edges may not be the shortest if the edges it contains are expensive.

**What is an unweighted graph?**

If edges in your graph have weights then your graph is said to be a weighted graph, if the edges do not have weights, the graph is said to be unweighted. A weight is a numerical value attached to each individual edge.

## Which algorithm can be used to find shortest path graph?

Dijkstra’s algorithm

Dijkstra’s algorithm (/ˈdaɪkstrəz/ DYKE-strəz) is an algorithm for finding the shortest paths between nodes in a graph, which may represent, for example, road networks. It was conceived by computer scientist Edsger W. Dijkstra in 1956 and published three years later.

## How do you find the shortest path between two vertices on a weighted graph?

One common way to find the shortest path in a weighted graph is using Dijkstra’s Algorithm. Dijkstra’s algorithm finds the shortest path between two vertices in a graph. It can also be used to generate a Shortest Path Tree – which will be the shortest path to all vertices in the graph (from a given source vertex).

**How many shortest paths are there in a graph?**

For example consider the below graph. There is one shortest path vertex 0 to vertex 0 (from each vertex there is a single shortest path to itself), one shortest path between vertex 0 to vertex 2 (0->2), and there are 4 different shortest paths from vertex 0 to vertex 6:

**What is the shortest path length of the input vertex?**

Output: Shortest path length is:2 Path is:: 0 3 7 Input: source vertex is = 2 and destination vertex is = 6. Output: Shortest path length is:5 Path is:: 2 1 0 3 4 6 Recommended: Please try your approach on {IDE} first, before moving on to the solution. One solution is to solve in O (VE) time using Bellman–Ford.

### What is the shortest path from a to F?

And so we find that the shortest path between A and F is 2. The tree not only tells you how long that path is, but also how to actually get from A to F (or any of the other nodes).

### How do you solve a graph with an unweighted vertex?

Since the graph is unweighted, we can solve this problem in O (V + E) time. The idea is to use a modified version of Breadth-first search in which we keep storing the predecessor of a given vertex while doing the breadth-first search.