# Dijkstra’s Algorithm for Distance and Shortest Paths in Weighted Graphs

*Computing spatial relationships between nodes when edges no longer represent unit distances.*

*This post is part of the book Introduction to Algorithms and Machine Learning: from Sorting to Strategic Agents.* *Suggested citation:* Skycak, J. (2022). Dijkstra's Algorithm for Distance and Shortest Paths in Weighted Graphs. In *Introduction to Algorithms and Machine Learning: from Sorting to Strategic Agents.* https://justinmath.com/dijkstras-algorithm-for-distance-and-shortest-paths-in-weighted-graphs/

In a **weighted graph**, every edge is assigned a value called a **weight**.

## Initializing a Weighted Graph

For example, consider the following weighted graph:

A weighted graph can be initialized with a weights dictionary instead of an edges list. The edges list just had a list of edges, whereas the weights dictionary will have its keys as edges and its values as the weights of those edges.

```
>>> weights = {
(0,1): 3, (1,0): 3,
(1,7): 4, (7,1): 4,
(7,2): 2, (2,7): 2,
(2,5): 1, (5,2): 1,
(5,6): 8, (6,5): 8,
(0,3): 2, (3,0): 2,
(3,2): 6, (2,3): 6,
(3,4): 1, (4,3): 1,
(4,8): 8, (8,4): 8,
(8,0): 4, (0,8): 4
}
>>> weighted_graph = WeightedGraph(weights)
```

## Distance and Shortest Paths in Weighted Graphs

In a weighted graph, the distance between two nodes is the sum of the weights on the shortest path between them. For example, the distance from node $8$ to node $4$ is $7$ because the shortest path is $8 \stackrel{4}{\to} 0 \stackrel{2}{\to} 3 \stackrel{1}{\to} 4.$

In particular, notice that the shortest path is NOT $8 \stackrel{8}{\to} 4$ because the distance along this path is $8.$

```
>>> [weighted_graph.calc_distance(8,n) for n in range(9)]
[4, 7, 12, 6, 7, 13, 21, 11, 0]
>>> weighted_graph.calc_shortest_path(8,4)
[8, 0, 3, 4]
>>> weighted_graph.calc_shortest_path(8,7)
[8, 0, 1, 7]
>>> weighted_graph.calc_shortest_path(8,6)
[8, 0, 3, 2, 5, 6]
```

## Dijkstra's Algorithm for Distance

The underlying algorithms for `calc_distance`

and `calc_shortest_path`

are a bit more complicated for weighted graphs than for unweighted graphs.

To implement `calc_distance(from_idx, to_idx)`

we need to use **Dijkstra’s algorithm**, which works by assigning each node an initial guess for its distance and then repeatedly updating those guesses until they actually represent the distances to those nodes.

- When setting initial guesses, the "from" node is assigned a distance value of $0$, and all other nodes are assigned distance values of $\infty$ (just use a large number like $9999999999$).
- Set the
`current_node`

to be the "from" node. - Loop through the
`current_node`

's unvisited children and update their distances as`child.distance = min(child.distance, current_node.distance + edge_weight)`

. - Update the
`current_node`

to be the*unvisited*node with the*smallest*distance value (not necessarily a child node). - If the ending node has not been visited yet, then return to step $3.$
- Return the
`distance`

attribute of the "to" node.

Let’s demonstrate each iteration of Dijkstra’s algorithm when computing the distance from node $8$ to node $6.$

First, we set the initial guesses for the distance values. Since we’re starting at node $8,$ we already know that it’s a distance of $0$ from itself. All the other nodes get initial guesses of infinity.

Now, we visit node 8 (the “from” node) and update the distance values on its children.

The next node we visit should be the unvisited node with the smallest distance value. This would be node $0,$ whose distance value is $4.$ We visit this node and update the distance values on its unvisited children.

Again, we visit the unvisited node with the smallest distance value. This time, it’s node $3.$

Notice that when we update the distance values on its unvisited children, node $4$’s distance value decreases from $8$ to $7.$ Whenever a distance value decreases like this, it means that the nodes we’ve traversed contain a shorter path than a path we found earlier.

In our first iteration, we found a path $8 \stackrel{8}{\to} 4$ with distance $8.$ Now, we found a path $8 \stackrel{4}{\to} 0 \stackrel{2}{\to} 3 \stackrel{1}{\to} 4$ with distance $7.$

As usual, we visit the unvisited node with the smallest distance value. This time, we can visit either node $1$ or node $4$ (they are the unvisited nodes with the smallest distance values). We’ll arbitrarily choose to visit node $1$ and update the distance values of its children.

We keep on repeating this same procedure until the “to” node (node $6$) has been visited.

We’ve visited node $6,$ and its distance value is $21.$ This means that the distance from node 8 to node 6 is $21.$

## Computing Shortest Paths via the Shortest-Path Tree

But what is the specific shortest path from node $8$ to node $6$ that gives us this distance of $21?$

To find the shortest path, we first construct the **shortest-path tree** by discarding any edge `(a,b)`

whose weight does not match the corresponding difference between distance values, `b.distance - a.distance`

.

Once we have the shortest-path tree, we’ve effectively reduced our problem down to a problem that we’ve already solved: finding the shortest path between two nodes in an unweighted graph.

Indeed, we can see that the shortest path from node $8$ to node $6$ in the tree above is given by $8 \to 0 \to 3 \to 2 \to 5 \to 6.$ And indeed, in the weighted graph, the sum of weights along this path is $21.$

## Exercises

Implement a `WeightedGraph`

class with the methods `calc_distance`

and `calc_shortest_path`

. As always, be sure to write tests.

*This post is part of the book Introduction to Algorithms and Machine Learning: from Sorting to Strategic Agents.* *Suggested citation:* Skycak, J. (2022). Dijkstra's Algorithm for Distance and Shortest Paths in Weighted Graphs. In *Introduction to Algorithms and Machine Learning: from Sorting to Strategic Agents.* https://justinmath.com/dijkstras-algorithm-for-distance-and-shortest-paths-in-weighted-graphs/