Applications, Advantages and Disadvantages of Unweighted Graph
Unweighted graph is a type of graph with no edge weight. In an unweighted graph, the edges represent the connection between two nodes. If there is an edge between nodes u and v in an unweighted graph then u and v are adjacent to each other.
Applications of Unweighted Graph:
- Unweighted graphs are used to represent data that are not related in terms of magnitude.
- Unweighted graphs are used to represent computation flow.
Real-Time Applications of Unweighted Graph:
- Unweighted graphs can be used to solve puzzles.
- It can be used to represent a circuit diagram.
- It can be used in social media sites to find whether two users are connected or not.
- It is used in Hamiltonian graphs which have many practical applications like genome mapping to combine many tiny fragments of genetic code.
Advantages of Unweighted Graph:
- Unweighted graphs can be used to implement tree data structures.
- Unweighted graphs are used in many algorithms like DFS and BFS.
- Helps in optimal visualization of interrelated problems which are not related in terms of magnitude.
- The unweighted graphs do not have edge weight. Hence, cannot be used for shortest path evaluation or applications which require the distance between the nodes.