1. Graph Operations And Representation

2. Path Finding Path between 1 and 8. Path length is 20. 2 3 8 1 10 4 5 911 6 7
Vertices represent cities and edges represent highways. Edge weights are distances or driving times. Depending on the context, path length may either be the number of edges on the path or the sum of the weights of the edges on the path.
Path length is 20.

3. Another Path Between 1 and 82 3 8 10 1 4 5 9 11 6 7
Since a graph may have more than one path between two vertices, we may be interested in finding a path with a particular property. For example, find a path with minimum length
Path length is 28.

4. Example Of No Path 2 3 8 10 1 4 5 9 11 6 7
No path between 2 and 9.

5. Connected Graph Undirected graph.
There is a path between every pair of vertices.

6. Example Of Not Connected2 3 8 10 1 4 5 9 11 6 7

7. Connected Graph Example2 3 8 10 1 4 5 9 11 6 7
Determine whether an undirected graph is connected.

8. Communication Network2 3 8 10 1 4 5 9 11 6 7
Each edge is a link that can be constructed (i.e., a feasible link).

9. Communication Network Problems
Is the network connected?
Can we communicate between every pair of cities?
Find the components.
Want to construct smallest number of feasible links so that resulting network is connected.

10. Cycles And Connectedness2 3 8 10 1 4 5 9 11 6 7
Removal of an edge that is on a cycle does not affect connectedness.

11. Cycles And Connectedness2 3 8 1 10 4 5 9 11 6 7
Connected subgraph with all vertices and minimum number of edges has no cycles.

12. Minimum Cost Spanning Tree2 3 8 10 1 4 5 9 11 6 7
Tree cost is sum of edge weights/costs.

13. A Spanning Tree Spanning tree cost = 51. 2 4 3 8 8 1 6 10 2 4 5 4 4 39 8 11 5 6 2 7 6 7
Spanning tree cost = 51.

14. Minimum Cost Spanning Tree2 4 3 8 8 1 6 10 2 4 5 4 4 3 5 9 8 11 5 6 2
In the communication networks area, we are interested in finding minimum cost spanning trees. 7 6 7
Spanning tree cost = 41.

15. Graph Representation Adjacency Matrix Adjacency Lists
Linked Adjacency Lists
Array Adjacency Lists

16. Adjacency Matrix 0/1 n x n matrix, where n = # of vertices
A(i,j) = 1 iff (i,j) is an edge 1 2 3 4 5 2 3 1 4 5 1 1 1 1 1 1 1 1 1 1

17. Adjacency Matrix Properties2 3 1 4 5
Diagonal entries are zero.
Adjacency matrix of an undirected graph is symmetric.

18. Adjacency Matrix (Digraph)2 3 1 4 5
Diagonal entries are zero.
Adjacency matrix of a digraph need not be symmetric.

19. The number of 1’s in the matrix equals twice the number of edges in the graph.

20. Adjacency Matrix n2 bits of space
For an undirected graph, may store only lower or upper triangle (exclude diagonal).
(n-1)n/2 bits
O(n) time to find vertex degree and/or vertices adjacent to a given vertex.

21. Adjacency Lists
Adjacency list for vertex i is a linear list of vertices adjacent from vertex i.
An array of n adjacency lists.
aList[1] = (2,4)
aList[2] = (1,5)
aList[3] = (5)
aList[4] = (5,1)
aList[5] = (2,4,3) 2 3 1 4 5

22. Linked Adjacency Lists
Each adjacency list is a chain. 2 3 1 4 5
aList[1]
aList[5]
[2]
[3]
[4] 2 4 1 5 5 5 1 2 4 3
Array length n simply means we need an array with n spots. A direct implementation using a Java array would need n+1 spots, because spot 0 would not be utilized. However, by using spot 0 for vertex 1, spot 1 for vertex 2, and so on, we could get by with a Java array whose length is actually n.
Array Length = n
# of chain nodes = 2e (undirected graph)
# of chain nodes = e (digraph)

23. Array Adjacency Lists Each adjacency list is an array list. aList[1]2 3 1 4 5
aList[1]
aList[5]
[2]
[3]
[4]
Array Length = n
# of list elements = 2e (undirected graph)
# of list elements = e (digraph)

24. Weighted Graphs Cost adjacency matrix.
C(i,j) = cost of edge (i,j)
Adjacency lists => each list element is a pair (adjacent vertex, edge weight)