15-111 Lecture 24 (Wednesday, April 1, 2009)

Spanning Trees

A Spanning Tree is a tree created from a graph that contains all of the nodes of the graph, but no cycles. In other words, you can create a minimum spanning tree from a graph, by removing one edge from any path that forms a cycle.

In the end, this will leave you with another graph, that is also a tree. If this graph contains N nodes, it will contain N-1 edges.

It contains at least N-1 edges, because this many are required to attach each node to the graph. Think of it this way. If the graph contains 1 node, there is nothing to attach it to -- as a result, no edges are needed. If we add nodes to the graph one at a time, we will need to use an edge to attach the new node to the graph. If we add a node, without adding an edge, we've really created a new graph with only one node -- we haven't expanded the old one, because we haven't established a relationship between the new node and any node in the old graph.

It contains at most N-1 edges, because more than one edge per node will create cycles, which would violate the definition of a tree. Imagine that you have a graph, any graph, with the minimum number, N-1, edges. Draw out your imaginary graph.

Now, try to add another edge to your graph, without creating a cycle. You can't do it. The reason is that each node, is already connected to another node of the graph. Adding an additional edge will connected it to a second point on the same graph. As a result, the graph will have a cycle -- it is no longer a tree.

Creating Spanning trees: Traversing Graphs

Today we are going to talk about two different ways of creating minimum spanning trees: the Depth-First Search and the Breadth-First Search. These algorithms are going to be very similar to the algorithms we studied for traversing trees.

The big difference is that we are going to mark the edges as we visit them, and avoid revisiting marked nodes. This is necessary, because graphs may have cycles -- and we don't want to go around-and-around ground we have already covered. So, if we leave a trail of breadcrumbs, and avoid recovering the ground we've already walked.

We can construct a spanning tree by keeping track of the path we follow to reach each node. In other words, we can start out with an empty graph, in addition to the one that we want to traverse. Then, instead of, or in addition to, printing each node we reach during the traversal, we can also add it to the new graph, by creating the same edge that we followed in the traversal to reach it.

So, if we get to Node-X from Node-Y, we then add an edge to Node-X from Node-Y in our new graph. Then, in the end, the new graph has the same nodes as the old graph -- but only one edge connecting each pair.

Depth First Search

Depth First Search (DFS) is a generalization of the preorder traversal. Starting at some arbitrarily chosen vertex v, we mark v so that we know we've visited it, process v, and then recursively traverse all unmarked vertices adjacent to v (v will be a different vertex with every new method call).

When we visit a vertex in which all of its neighbors have been visited, we return to its calling vertex, and visit one of its unvisited neighbors, repeating the recursion in the same manner. We continue until we have visited all of the starting vertex's neighbors, which means that we're done. The recursion (stack) guides us through the graph.

Performing a DFS on the following graph: 1. Any vertex can be the starting vertex. We choose to visit 1 first. (push 1)
2. From 1, we can go on to 0, 2, or 3.
3. We visit 0. (push 0) 
4. From 0, we can go on to 4 or 5 (we've already been to 1). We visit 4. (push 4) [4 0]
5. From 4, we can go on to 3 or 5 (we've already been to 0). We visit 3. (push 3) [3 4 0]
6. From 3, we can go on to 2 or 4 (we've already been to 1). We visit 2. (push 2) [2 3 4 0]

7. Now we've gone as far as we can go (from 2, we've already visited both 1 and 3). We can start returning.

9. We've already been to 1, 2, and 4, so we return.

11. We've already been to 0 and 3, but not to 5. We visit 5. (push 5) [5 4 0] (pop 5, pop 4, pop 0) []

12. We're done.

Pseudocode for DFS

``` ```

``````public void depthFirstSearch(Vertex v)
{
v.visited = true;
// print the node or add it to the new spanning tree here
for(each vertex w adjacent to v)
if(!w.visited)
depthFirstSearch(w);
}
``````
``` ```

Breadth First Search (BFS) searches the graph one level (one edge away from the starting vertex) at a time. In this respect, it is very similar to the level order traversal that we discussed for trees.

Starting at some arbitrarily chosen vertex v, we mark v so that we know we've visited it, process v, and then visit and process all of v's neighbors.

Now that we've visited and processed all of v's neighbors, we need to visit and process all of v's neighbors neighbors. So we go to the first neighbor we visited and visit all of its neighbors, then the second neighbor we visited, and so on. We continue this process until we've visited all vertices in the graph. We don't use recursion in a BFS because we don't want to traverse recursively. We want to traverse one level at a time.

So imagine that you visit a vertex v, and then you visit all of v's neighbors w. Now you need to visit each w's neighbors. How are you going to remember all of your w's so that you can go back and visit their neighbors? You're already marked and processed all of the w's. How are you going to find each w's neighbors if you don't remember where the w's are? After all, you're not using recursion, so there's no stack to keep track of them.

To perform a BFS, we use a queue. Every time we visit vertex w's neighbors, we dequeue w and enqueue w's neighbors. In this way, we can keep track of which neighbors belong to which vertex. This is the same technique that we saw for the level-order traversal of a tree. The only new trick is that we need to makr the verticies, so we don't visit them more than once -- and this isn't even new, since this technique was used for the blobs problem during our discussion of recursion.

Performing a BFS on the same graph: 1. We choose to start by visiting 1. (enqueue 1) 

2. We visit 0, 2, and 3 because they are all one step away. (dequeue 1, enqueue 0, enqueue 2, enqueue 3) [0 2 3]

3. Because we visited 0 first, we go back to 0 and visit its neighbors, 4 and 5. (dequeue 0, enqueue 4, enqueue 5) [2 3 4 5] 2 and 3 have no unvisited neighbors. (dequeue 2, dequeue 3, dequeue 4, dequeue 5) [ ]

4. We're done.

Pseudocode

``` public void breadthFirstSearch(vertex v) { Queue q = new Queue(); v.visited = true; q.enQueue(v); while( !q.isEmpty() ) { Vertex w = (Vertex)q.deQueue(); // Pritn the node or add it to the spanning tree here. for(each vertex x adjacent to w) { if( !x.visited ) { x.visited = true; q.enQueue(x); } } } } ```