# What is Weighted Graph in Data Structure?

A weighted graph is a graph data structure in which each edge has a numerical weight or cost associated with it. The weight represents some attribute or quantity associated with the edge, such as distance, cost, time, or capacity. For example, in a transportation network, the weight of an edge can represent the distance between two cities or the time it takes to travel from one city to another.

• Weighted graphs are used to model a wide range of real-world systems and networks, including social networks, communication networks, power grids, supply chains, and transportation networks. They are especially useful when we need to capture additional information beyond the presence or absence of a connection between two nodes.
• A weighted graph can be represented using various data structures, including adjacency matrix, adjacency list, and edge list. In an adjacency matrix representation, we use a matrix to store the weights of the edges between each pair of vertices. For example, a[i][j] represents the weight of the edge between vertex i and vertex j. If there is no edge between two vertices, the weight is usually set to infinity.
• In an adjacency list representation, we use a list or array to store the edges and their weights for each vertex. For example, we can represent the edges of vertex as a list of tuples (j, w), where j is the other vertex and w is the weight of the edge. The advantage of this representation is that it allows us to efficiently iterate over the edges of a vertex and find the weight of a specific edge.
• In an edge list representation, we use a list of tuples or triplets to store the edges and their weights. Each tuple or triplet represents an edge and its weight, for example (i, j, w) represents an edge between vertex i and vertex j with weight w. The advantage of this representation is that it is compact and easy to store and manipulate.
• Algorithms that work with weighted graphs often require additional processing to handle the weights. For example, to find the shortest path between two vertices in a weighted graph, we can use Dijkstra's algorithm, which uses a priority queue to keep track of the vertices with the lowest distance from the source vertex. To find the minimum spanning tree of a weighted graph, we can use Kruskal's algorithm, which starts with an empty set of edges and adds the edges with the lowest weight until all vertices are connected. Other popular algorithms for weighted graphs include Bellman-Ford algorithm, Floyd-Warshall algorithm, and A* search algorithm.

## Advantages of using weighted graphs

• More accurate representation: Weighted graphs provide a more accurate representation of the real world, where the distance or cost of moving between two points is not always the same. This makes them useful for modelling various applications, such as transportation planning, network routing, and scheduling.
• Better decision making: Weighted graphs can help make better decisions by considering the cost or distance of moving between two points. For example, in a transportation network, a weighted graph can be used to find the fastest or cheapest route between two locations, helping to optimize the use of resources and reduce costs.
• Efficient algorithms: There are efficient algorithms available for solving problems on weighted graphs, such as Dijkstra's algorithm and the Bellman-Ford algorithm. These algorithms can be used to find the shortest path between two points, which is a common problem in many applications.

## Disadvantages of using weighted graphs

• Increased complexity: Weighted graphs are more complex than unweighted graphs, as each edge has an associated weight. This can make them more difficult to understand and analyze, especially for those who are new to graph theory.
• Higher storage requirements: Weighted graphs require additional storage space to store the weights associated with each edge. This can be a concern when dealing with large graphs or limited storage space.
• More computation required: As each edge has an associated weight, more computation is required to perform operations on weighted graphs. For example, finding the shortest path between two nodes in a weighted graph requires more computation than in an unweighted graph.

In summary, weighted graphs have several advantages, such as providing a more accurate representation of real-world scenarios and enabling better decision-making. However, they also have some disadvantages, such as increased complexity, higher storage requirements, and more computation required. It is important to consider these factors when deciding whether to use a weighted graph for a particular application.

Here's an example of a weighted graph:

4       2

A ------ B ------ C

|       / \       |

|      3   1      |

|     /     \     |

|    /       \    |

|   D         E   |

|  / \       / \  |

| 7   2     6   5 |

| /   |     |   \ |

|/    |     |    \|

F --- G     H --- I

8       4

In this graph, each node represents a location, and the edges between nodes represent a connection or path between them. However, unlike an unweighted graph, each edge has a weight assigned to it, which represents the cost or distance of traversing that edge. For example, the edge between nodes A and D has a weight of 7, which means it costs 7 units to travel from A to D. Similarly, the edge between nodes B and E has a weight of 6, which means it costs 6 units to travel from B to E.

Weighted graphs can be implemented using various data structures, including adjacency lists, adjacency matrices, or edge lists. One popular algorithm for finding the shortest path in a weighted graph is Dijkstra's algorithm, which works by iteratively finding the shortest path from the source node to all other nodes in the graph. Other algorithms for weighted graphs include the Bellman-Ford algorithm and the Floyd-Warshall algorithm.

### Conclusion

In summary, a weighted graph is a type of graph where each edge has a weight or cost associated with it. Weighted graphs can be used to model various applications, and they are commonly implemented using data structures such as adjacency lists or matrices.