Representation
Last updated
Last updated
To store the info about a graph, there are two general approaches. We will use the following digraph in for examples in each of the following sections.
An adjacency matrix is in essence a 2 dimensional array. Each index value represents a node. When given 2 nodes, you can find out whether or not they are connected by simply checking if the value in corresponding array element is 0 or not. For graphs without weights, 1 represents a connection. 0 represents a non-connection.
When a graph is dense, the graph adjancey matrix is a good represenation if the graph is dense. It is not good if the graph is sparse as many of the values in the array will be 0.
A-0 | B-1 | C-2 | D-3 | E-4 | F-5 | G-6 | |
A-0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 |
B-1 | 0 | 0 | 0 | 0 | 4 | 0 | 0 |
C-2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 |
D-3 | 0 | 0 | 1 | 0 | 0 | 0 | 0 |
E-4 | 0 | 0 | 0 | 2 | 0 | 4 | 0 |
F-5 | 0 | 0 | 0 | 0 | 3 | 0 | 1 |
G-6 | 0 | 5 | 0 | 0 | 0 | 0 | 0 |
An adjacency list uses an array of linked lists to represent a graph Each element represents a vertex. For each vertex it is connected to, a node is added to it's linked list. For graphs with weights each node also stores the weight of the connection to the node. Adjacency lists are much better if the graph is sparse.
