need ans with output An unweighted graph G = (V, E) does not have weights associated with its edges as noted. A simple strategy to find the maximal matching in a greedy way is to randomly select an edge in the graph, include it in the matching and remove all of its adjacent edges from the graph as in the steps below. 1. Input: An unweighted graph G = (V, E) 2. Output: A maximal matching M of G 3. M ← Ø 4. E ← E 5. while E = Ø 6. randomly select e ∈ E 7. M ← M ∪ {e} 8. E ← E \ {e∪ all adjacent edges to e} implement this algorithm in Python

need ans with output An unweighted graph G = (V, E) does not have weights associated with its edges as noted. A simple strategy to find the maximal matching in a greedy way is to randomly select an edge in the graph, include it in the matching and remove all of its adjacent edges from the graph as in the steps below. 1. Input: An unweighted graph G = (V, E) 2. Output: A maximal matching M of G 3. M ← Ø 4. E ← E 5. while E = Ø 6. randomly select e ∈ E 7. M ← M ∪ {e} 8. E ← E \ {e∪ all adjacent edges to e} implement this algorithm in Python

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Concept explainers

Minimum Spanning Tree

A spanning tree is a sub-graph of a connected, undirected graph. It has all the vertices of a graph with the minimum possible number of edges. If a vertex is skipped, then it will not be a spanning tree. The edges of a spanning tree may or may not have weights.

Question

need ans with output

An unweighted graph G = (V, E) does not have weights associated with its edges as
noted. A simple strategy to find the maximal matching in a greedy way is to randomly
select an edge in the graph, include it in the matching and remove all of its adjacent
edges from the graph as in the steps below.
1. Input: An unweighted graph G = (V, E)
2. Output: A maximal matching M of G
3. M ← Ø
4. E ← E
5. while E = Ø
6. randomly select e ∈ E
7. M ← M ∪ {e}
8. E ← E \ {e∪ all adjacent edges to e}
implement this algorithm in Python

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