Presentation on theme: "Graphs Chapter 30 Carrano, Data Structures and Abstractions with Java, Second Edition, (c) 2007 Pearson Education, Inc. All rights reserved. 0-13-237045-X."— Presentation transcript:
Graphs Chapter 30 Carrano, Data Structures and Abstractions with Java, Second Edition, (c) 2007 Pearson Education, Inc. All rights reserved. 0-13-237045-X
Chapter Contents Some Examples and Terminology – Road Maps – Airline Routes – Mazes – Course Prerequisites – Trees Traversals – Breadth-First Traversal – Dept-First Traversal
Chapter Contents Topological Order Paths – Finding a Path – Shortest Path in an Unweighted Graph – Shortest Path in a Weighted Graph Java Interfaces for the ADT Graph
Some Examples and Terminology Vertices or nodes are connected by edges A graph is a collection of distinct vertices and distinct edges – Edges can be directed or undirected – When it has directed edges it is called a digraph A subgraph is a portion of a graph that itself is a graph
Road Maps Fig. 30-1 A portion of a road map. Nodes Edges
Road Maps Fig. 30-2 A directed graph representing a portion of a city's street map.
Paths A sequence of edges that connect two vertices in a graph In a directed graph the direction of the edges must be considered – Called a directed path A cycle is a path that begins and ends at same vertex – Simple path does not pass through any vertex more than once A graph with no cycles is acyclic
Weights A weighted graph has values on its edges – Weights or costs A path in a weighted graph also has weight or cost – The sum of the edge weights Examples of weights – Miles between nodes on a map – Driving time between nodes – Taxi cost between node locations
Adjacent Vertices Two vertices are adjacent in an undirected graph if they are joined by an edge Sometimes adjacent vertices are called neighbors Fig. 30-5 Vertex A is adjacent to B, but B is not adjacent to A.
Airline Routes Note the graph with two subgraphs – Each subgraph connected – Entire graph disconnected Fig. 30-6 Airline routes
Mazes Fig. 30-7 (a) A maze; (b) its representation as a graph
Course Prerequisites Fig. 30-8 The prerequisite structure for a selection of courses as a directed graph without cycles.
Trees All trees are graphs – But not all graphs are trees A tree is a connected graph without cycles Traversals – Preorder, inorder, postorder traversals are examples of depth-first traversal – Level-order traversal of a tree is an example of breadth-first traversal Visit a node – For a tree: process the node's data – For a graph: mark the node as visited
Trees Fig. 30-9 The visitation order of two traversals; (a) depth first
Trees Fig. 30-9 The visitation order of two traversals; (b) breadth first.
Breadth-First Traversal A breadth-first traversal – visits a vertex and – then each of the vertex's neighbors – before advancing View algorithm for breadth-first traversal of nonempty graph beginning at a given vertex View algorithm
Breadth-First Traversal Fig. 30-10 (ctd.) A trace of a breadth-first traversal for a directed graph, beginning at vertex A.
Depth-First Traversal Visits a vertex, then – A neighbor of the vertex, – A neighbor of the neighbor, – Etc. Advance as possible from the original vertex Then back up by one vertex – Considers the next neighbor View algorithm for depth-first traversal View algorithm
Depth-First Traversal Fig. 30-11 A trace of a depth-first traversal beginning at vertex A of the directed graph
Topological Order Given a directed graph without cycles In a topological order – Vertex a precedes vertex b whenever – A directed edge exists from a to b
Topological Order Fig. 30-12 Three topological orders for the graph of Fig. 30-8. Fig. 30-8
Topological Order Fig. 30-13 An impossible prerequisite structure for three courses as a directed graph with a cycle. Click to view algorithm for a topological sort
Topological Order Fig. 30-14 Finding a topological order for the graph in Fig. 30-8.
Shortest Path in an Unweighted Graph Fig. 30-15 (a) an unweighted graph and (b) the possible paths from vertex A to vertex H.
Shortest Path in an Unweighted Graph Fig. 30-16 (a) The graph in 30-15a after the shortest-path algorithm has traversed from vertex A to vertex H; (b) the data in the vertex Click to view algorithm for finding shortest path
Shortest Path in an Unweighted Graph Fig. 30-17 Finding the shortest path from vertex A to vertex H in the unweighted graph
Shortest Path in an Weighted Graph Fig. 30-18 (a) A weighted graph and (b) the possible paths from vertex A to vertex H.
Shortest Path in an Weighted Graph Shortest path between two given vertices – Smallest edge-weight sum Algorithm based on breadth-first traversal Several paths in a weighted graph might have same minimum edge-weight sum – Algorithm given by text finds only one of these paths
Shortest Path in an Weighted Graph Fig. 30-19 Finding the cheapest path from vertex A to vertex H in the weighted graph
Shortest Path in an Weighted Graph Fig. 30-20 The graph in Fig. 30-18a after finding the cheapest path from vertex A to vertex H. Click to view algorithm for finding cheapest path in a weighted graph
Java Interfaces for the ADT Graph Methods in the BasicGraphInterface – addVertex – addEdge – hasEdge – isEmpty – getNumberOfVertices – getNumberOfEdges – clear View interface for basic graph operations View interface
Java Interfaces for the ADT Graph Fig. 30-21 A portion of the flight map in Fig. 30-6.
Java Interfaces for the ADT Graph Operations of the ADT – Graph enable creation of a graph and – Answer questions based on relationships among vertices View interface of operations on an existing graph View interface