Transcript Graphs CSC 220 Data Structure Introduction • One of the Most versatile data structures like trees. • Terminology – Nodes in trees are vertices in graphs. –

Graphs
CSC 220
Data Structure
Introduction
• One of the Most versatile data structures
like trees.
• Terminology
– Nodes in trees are vertices in graphs.
– Lines connecting vertices are edges.
– Each edge is bounded by two vertices at its
ends.
– Two vertices are said to be adjacent to one
another if they are connected by a single
edge.
Introduction
(Terminology)
• Vertices adjacent to a given vertex are
called its neighbors.
• A path is a sequence of edges.
• A graph is said to be connected if there is
at least one path from every vertex to
every other vertex.
• A non-connected graph consists of
several connected components.
Sample of graph app
Connected vs Nonconnected graph
Connected graph
Non-Connected graph
Directed and
Weighted Graphs
• Undirected graphs - edges don’t have a
direction.
– Can travel in any direction.
• Directed graphs – can traverse in only the
indicated direction.
• Weight – number that represents physical
distance or time or cost between two
vertices.
Sample of graph app
Representing Graphs
• Vertices can be represented by a vertex
class (i.e.similar to node or link in LL).
• Vertex objects can be placed in
– an array,
– list or
– other data structure.
• Graphs can be represented using
adjacency matrix or adjacency list.
Representing Graphs
1. Adjacency matrix – 2-dimensional array
in which elements indicate whether an
edge is present between two vertices.
– Edge is represented by 1.
– Or Edge is represented by weight
2. Adjacency List – array of lists or list of
lists.
– Each individual list shows what vertices a
given vertex is adjacent to.
Go to the white
board
Search
• Fundamental operation, to find out which vertices
can be reached from a specified vertex.
• An algorithm that provides a systematic way to
start at a specified vertex and then move along
edges to other vertex.
• Every vertex that is connected to this vertex has
to be visited at the end.
• Two common approaches :
– depth-first (DFS) and
– breadth-first search (BFS).
DFS
• Can be implemented with a stack to
remember where it should go when it
reaches a dead end.
• DFS goes far way from the starting point
as quickly as possible and returns only if it
reaches a dead end.
• Used in simulations of games
DFS
There are 3 rules
-- start with a vertex
R1 a. go to any vertex adjacent to it
that hasn’t yet been visited
b. push it on a stack and mark it
R2 if can’t follow R1, then possible
pop up a vertex off the stack
R3 if can’t follow R1 and R2, you’re
done
DFS
• Steps:
-- start with a vertex
-- visit it
-- push it on a stack and mark it
-- go to any vertex adjacent to it
that hasn’t yet been visited
-- if there are no unvisited nodes,
pop a vertex off the stack till
you come across a vertex that
has unvisited adjacent vertices
Breadth-First Search
BFS
• Implemented with a queue.
• Stays as close as possible to the starting
point.
• Visits all the vertices adjacent to the
starting vertex
BFS
• Steps:
-- start with visiting a starting
vertex
-- visit the next unvisited
adjacent vertex, mark it
and insert it into the
queue.
-- if there are no unvisited
vertices, remove a vertex
from the queue and make
it the current vertex.
-- continue until you reach
the end.
Minimum Spanning
Tree(MST)
MST
• DSF application -> display visited edges
• Example Application
– Reduce paths and pins in VLSI (Chip) design
and fabrication