Data structure computer graphs

What is a graph?
• A data structure that consists of a set of nodes
(vertices) and a set of edges that relate the nodes
to each other
• The set of edges describes relationships among the
vertices

Formal definition of graphs
• A graph G is defined as follows:
G=(V,E)
V(G): a finite, nonempty set of vertices
E(G): a set of edges (pairs of vertices)

Directed vs. undirected graphs
• When the edges in a graph have no
direction, the graph is called undirected

• When the edges in a graph have a direction,
the graph is called directed (or digraph)
Directed vs. undirected graphs
(cont.)
E(Graph2) = {(1,3) (3,1) (5,9) (9,11)
(5,7)
Warning: if the graph is
directed, the order of the
vertices in each edge is
important !!

• Trees are special cases of graphs!!
Trees vs graphs

Graph terminology
• Adjacent nodes: two nodes are adjacent if
they are connected by an edge
• Path: a sequence of vertices that connect
two nodes in a graph
• Complete graph: a graph in which every
vertex is directly connected to every other
vertex
5 is adjacent to 7
7 is adjacent from 5

• What is the number of edges in a complete
directed graph with N vertices?
N * (N-1)
Graph terminology (cont.)

• What is the number of edges in a complete
undirected graph with N vertices?
N * (N-1) / 2

• Multi-graph
• Cycle
• Loop

• Weighted graph: a graph in which each edge
carries a value

Graph implementation
• Array-based implementation
– A 1D array is used to represent the vertices
– A 2D array (adjacency matrix) is used to
represent the edges

Array-based implementation-
Adjacency Matrix

Graph implementation (cont.)
• Linked-list implementation
– A 1D array is used to represent the vertices
– A list is used for each vertex v which contains the
vertices which are adjacent from v (adjacency list)

Adjacency Matrix for Un-
Weighted Graphs
1. Adjacency Matrix
2. Matrix for edges
3. Matrix for 2 length paths
4. Matrix for 3 length paths
5. Matrix for n length paths
6. Path Matrix
7. Completely Connected Graph

Graph searching
• Problem: find a path between two nodes of
the graph (e.g., Austin and Washington)
• Methods: Depth-First-Search (DFS) or
Breadth-First-Search (BFS)

Depth-First-Search (DFS)
• What is the idea behind DFS?
– Travel as far as you can down a path
– Back up as little as possible when you reach a
"dead end" (i.e., next vertex has been "marked"
or there is no next vertex)
• DFS can be implemented efficiently using a
stack

DFS
• Algorithm:
Starting Node is A
1. Initialize all nodes to ready state(Status=1)
2. Push starting nose A onto Stack and change its status to
waiting state (Status=2).
3.Repeat steps 4 & 5 until STACK is empty.
4. Pop Top node N. Process N and change its Status to the processed
state (Status=3).
5.Push onto Stack all the neighbors of N that are still in ready state
and change their state as waiting state(State=2).
6. Exit.

Breadth-First-Searching (BFS)
• What is the idea behind BFS?
– Look at all possible paths at the same depth
before you go at a deeper level
– Back up as far as possible when you reach a
"dead end" (i.e., next vertex has been "marked"
or there is no next vertex)

BFS
• Algorithm:
Starting Node is A
1. Initialize all nodes to ready state(Status=1)
2. Push starting nose A onto Queue and change its status to
waiting state (Status=2).
3.Repeat steps 4 & 5 until Queue is empty.
4. Remove the front node N. Process N and change its Status to the
processed state (Status=3).
5.Add to the rear of Queue all the neighbors of N that are still in
ready state and change their state to waiting (State=2).
6. Exit.

• Examples of Both DFS & BFS.

Data structure computer graphs

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