# What is a transitive closure of a graph?

## What is a transitive closure of a graph?

Transitive Closure it the reachability matrix to reach from vertex u to vertex v of a graph. When there is a value 1 for vertex u to vertex v, it means that there is at least one path from u to v.

**What is shortest path in a graph?**

Given a real-valued weight function , and an undirected (simple) graph , the shortest path from to is the path (where and ) that over all possible. minimizes the sum. When each edge in the graph has unit weight or. , this is equivalent to finding the path with fewest edges.

**How do you know if a directed graph is transitive?**

In the case of graphs, we say a graph is transitive if, for every triple of vertices a, b, and c, if (a, b) is an edge, and (b, c) is an edge, then (a, c) is also an edge.

### Which algorithm is used to find the transitive closure of the graph?

Warshall Algorithm

Warshall Algorithm is used to find transitive closure of a graph.

**What is transitive closure example?**

For example, if X is a set of airports and xRy means “there is a direct flight from airport x to airport y” (for x and y in X), then the transitive closure of R on X is the relation R+ such that x R+ y means “it is possible to fly from x to y in one or more flights”.

**How do you find a transitive closure?**

Proof: In order for R^{*} to be the transitive closure, it must contain R, be transitive, and be a subset of in any transitive relation that contains R. By the definition of R^{*}, it contains R. If there are (a,b),(b,c)\in R^{*}, then there are j and k such that (a,b)\in R^j and (b,c)\in R^k.

#### How do you find the shortest path?

- 5 Ways to Find the Shortest Path in a Graph. Dijkstra’s algorithm is not your only choice.
- Depth-First Search (DFS) This is probably the simplest algorithm to get the shortest path.
- Breadth-First Search (BFS)
- Bidirectional Search.
- Dijkstra’s Algorithm.
- Bellman-Ford Algorithm.

**What is the formula to complete the transitive closure of a graph?**

The reach-ability matrix is called the transitive closure of a graph. The graph is given in the form of adjacency matrix say ‘graph[V][V]’ where graph[i][j] is 1 if there is an edge from vertex i to vertex j or i is equal to j, otherwise graph[i][j] is 0.

**What is transitive closure give an example?**

For example, if X is a set of airports and xRy means “there is a direct flight from airport x to airport y” (for x and y in X), then the transitive closure of R on X is the relation R+ such that x R+ y means “it is possible to fly from x to y in one or more flights”. …

## How do you find the transitive closure?

The transitive closure of a relation can be found by adding new ordered pairs that must be present and then repeating this process until no new ordered pairs are needed. Then (0, 2) ∈ Rt and (2, 3) ∈ Rt, so since Rt is transitive, (0, 3) ∈ Rt.

**Which is an example of the transitive closure of a graph?**

Given a directed graph, find out if a vertex j is reachable from another vertex i for all vertex pairs (i, j) in the given graph. Here reachable mean that there is a path from vertex i to j. The reach-ability matrix is called the transitive closure of a graph. For example, consider below graph

**How to optimize the closure of a graph?**

The graph is given in the form of adjacency matrix say ‘graph [V] [V]’ where graph [i] [j] is 1 if there is an edge from vertex i to vertex j or i is equal to j, otherwise graph [i] [j] is 0. Instead of directly using Floyd Warshall, we can optimize it in terms of space and time, for this particular problem. Following are the optimizations:

### How to optimize the reach of a graph?

Following are the optimizations: Instead of an integer resultant matrix ( dist [V] [V] in floyd warshall ), we can create a boolean reach-ability matrix reach [V] [V] (we save space). The value reach [i] [j] will be 1 if j is reachable from i, otherwise 0.

**How to calculate the time complexity of a graph?**

Time complexity is the same though) Below is the implementation of the above approach: Time Complexity: O (V 3) where V is number of vertices in the given graph. See below post for a O (V 2) solution.