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Understanding Graphs: Concepts and Applications

Explore the fundamentals of graphs, edge types, applications, and terminology in data structures and algorithms. Learn about vertex, edge, directed and undirected graphs, and their real-world applications. Practice exercises included.

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Understanding Graphs: Concepts and Applications

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  1. Graphs Part 1 1843 ORD Presentation for use with the textbook Data Structures and Algorithms in Java, 6th edition, by M. T. Goodrich, R. Tamassia, and M. H. Goldwasser, Wiley, 2014 SFO 802 1743 337 1233 LAX DFW Graphs

  2. A graph is a pair (V, E), where V is a set of nodes, called vertices E is a collection of pairs of vertices, called edges Vertices and edges are positions and store elements Example: A vertex represents an airport and stores the three-letter airport code An edge represents a flight route between two airports and stores the mileage of the route Graphs 849 PVD 1843 ORD 142 SFO 802 LGA 1743 337 1387 HNL 2555 1099 1233 LAX 1120 DFW MIA Graphs

  3. Edge Types • Directed edge • ordered pair of vertices (u,v) • first vertex u is the origin • second vertex v is the destination • e.g., a flight • Undirected edge • unordered pair of vertices (u,v) • e.g., a flight route • Directed graph • all the edges are directed • e.g., route network • Undirected graph • all the edges are undirected • e.g., flight network flight AA 1206 ORD PVD weight 849 miles ORD PVD Graphs

  4. Applications • Electronic circuits • Printed circuit board • Integrated circuit • Transportation networks • Highway network • Flight network • Computer networks • Local area network • Internet • Web • Databases • Entity-relationship diagram Graphs

  5. V a b h j U d X Z c e i W g f Y Terminology • End vertices (or endpoints) of an edge • U and V are the endpoints of a • Edges incident on a vertex • a, d, and b are incident on V • Adjacent vertices • U and V are adjacent • Degree of a vertex • X has degree 5 • Parallel edges • h and i are parallel edges • Self-loop • j is a self-loop Graphs

  6. Terminology (cont.) • Path • sequence of alternating vertices and edges • begins with a vertex • ends with a vertex • each edge is preceded and followed by its endpoints • Simple path • path such that all its vertices and edges are distinct • Examples • P1=(V,b,X,h,Z) is a simple path • P2=(U,c,W,e,X,g,Y,f,W,d,V) is a path that is not simple V b a P1 d U X Z P2 h c e W g f Y Graphs

  7. Terminology (cont.) • Cycle • circular sequence of alternating vertices and edges • each edge is preceded and followed by its endpoints • Simple cycle • cycle such that all its vertices and edges are distinct • Examples • C1=(V,b,X,g,Y,f,W,c,U,a,) is a simple cycle • C2=(U,c,W,e,X,g,Y,f,W,d,V,a,) is a cycle that is not simple V a b d U X Z C2 h e C1 c W g f Y Graphs

  8. Exercise on Terminology 849 PVD 1843 ORD 142 SFO 802 LGA 1743 337 1387 2555 HNL 1099 1233 LAX 1120 DFW MIA Number of vertices? Number of edges? What type of the graph is it? Show the end vertices of the edge with largest weight Show the vertices of smallest degree and largest degree Show the edges incident to the vertices in the above question Identify the shortest simple path from HNL to PVD Identify the simple cycle with the most edges

  9. ExerciseProperties of Undirected Graphs • Property 1 – Total degree • Property 2 – Total number of edges • In an undirected graph with no self-loops and no multiple edges Each vertex can have degree at most • Notation • number of vertices • number of edges • degree of vertex v Example A graph with given number of vertices (4) and maximum number of edges

  10. Properties of Undirected Graphs • Property 1 – Total degree • Property 2 – Total number of edges • In an undirected graph with no self-loops and no multiple edges Proof: Each vertex can have degree at most • Notation • number of vertices • number of edges • degree of vertex v Example A graph with given number of vertices (4) and maximum number of edges

  11. E D C B A Directed Graphs (Digraphs) • A digraph is a graph whose edges are all directed • Short for “directed graph” • Applications • one-way streets • flights • task scheduling Directed Graphs

  12. Digraph Application • Scheduling: edge (a,b) means task a must be completed before b can be started cs21 cs22 cs46 cs51 cs53 cs52 cs161 cs131 cs141 cs121 cs171 The good life cs151 Directed Graphs

  13. ExerciseProperties of Directed Graphs • Property 1 – Total in-degree and out-degree • Property 2 – Total number of edges • In an directed graph with no self-loops and no multiple edges • Notation • number of vertices • number of edges • degree of vertex v Example A graph with given number of vertices (4) and maximum number of edges

  14. Properties of Directed Graphs • Property 1 – Total in-degree and out-degree • Property 2 – Total number of edges • In an directed graph with no self-loops and no multiple edges • Notation • number of vertices • number of edges • degree of vertex v Example A graph with given number of vertices (4) and maximum number of edges

  15. E D C B A Digraph Properties • A graph G=(V,E) such that • Each edge goes in one direction: • Edge (a,b) goes from a to b, but not b to a • If G is simple, m<n(n -1) • If we keep in-edges and out-edges in separate adjacency lists, we can perform listing of incoming edges and outgoing edges in time proportional to their size Directed Graphs

  16. TerminologyConnectivity Connected graph and are reachable Connected digraph and are not mutually reachable Given two vertices and , we say reaches, and that is reachable from , if there exists a path from to . In an undirected graph reachability is symmetric A graph is connected if there is a path between every pair of vertices A digraph is strongly connected if for any two vertices u and v of G, u reaches v and v reaches u

  17. TerminologySubgraphs Subgraph Spanning subgraph Non connected graph with two connected components A subgraph of a graph is a graph whose vertices and edges are subsets of A spanning subgraph of is a subgraph that contains all the vertices of A connected component of a graph is a maximal connected subgraph of

  18. TerminologyTrees and Forests • A forest is a graph without cycles • A (free) tree is connected forest • This definition of tree is different from the one of a rooted tree • The connected components of a forest are trees Tree Forest

  19. Spanning Trees and Forests • A spanning tree of a connected graph is a spanning subgraph that is a tree • A spanning tree is not unique unless the graph is a tree • Spanning trees have applications to the design of communication networks Graph Spanning tree

  20. A graph is a collection of vertices and edges. We model the abstraction as a combination of three data types: Vertex, Edge, and Graph. A Vertex is a lightweight object that stores an arbitrary element provided by the user (e.g., an airport code) We assume it supports a method, element(), to retrieve the stored element. An Edge stores an associated object (e.g., a flight number, travel distance, cost), retrieved with the element( ) method. Modeling Graphs Vertices and Edges Graphs

  21. Modeling Graphs Graph ADT Graphs

  22. Exercise on ADT • outgoingEdges • incomingEdges • outDegree • endVertices • opposite • insertVertex • insertEdge • removeVertex • removeEdge 849 PVD 1843 ORD 142 SFO 802 LGA 1743 337 1387 2555 HNL 1099 1233 LAX 1120 DFW MIA

  23. Edge List Structure • Vertex object • element • reference to position in vertex sequence • Edge object • element • origin vertex object • destination vertex object • reference to position in edge sequence • Vertex sequence • sequence of vertex objects • Edge sequence • sequence of edge objects Graphs

  24. Adjacency List Structure • Incidence sequence for each vertex • sequence of references to edge objects of incident edges • Augmented edge objects • references to associated positions in incidence sequences of end vertices Graphs

  25. Adjacency Matrix Structure • Edge list structure • Augmented vertex objects • Integer key (index) associated with vertex • 2D-array adjacency array • Reference to edge object for adjacent vertices • Null for non nonadjacent vertices • The “old fashioned” version just has 0 for no edge and 1 for edge Graphs

  26. Performance Also there is the “Adjacency Map Structure” Graphs

  27. A B D E C Depth-First Search Presentation for use with the textbook Data Structures and Algorithms in Java, 6th edition, by M. T. Goodrich, R. Tamassia, and M. H. Goldwasser, Wiley, 2014 Depth-First Search

  28. Depth-first search (DFS) is a general technique for traversing a graph A DFS traversal of a graph G Visits all the vertices and edges of G Determines whether G is connected Computes the connected components of G Computes a spanning forest of G DFS on a graph with n vertices and m edges takes O(n + m ) time DFS can be further extended to solve other graph problems Find and report a path between two given vertices Find a cycle in the graph Depth-first search is to graphs what Euler tour is to binary trees Depth-First Search Depth-First Search

  29. DFS Algorithm from a Vertex Depth-First Search

  30. Java Implementation Depth-First Search

  31. A B D E C A A B D E B D E C C Example unexplored vertex A visited vertex A unexplored edge discovery edge back edge Depth-First Search

  32. A A A B D E B D E B D E C C C A B D E C Example (cont.) Depth-First Search

  33. Exercise DFS Algorithm A B C D E F • Perform DFS of the following graph, start from vertex A • Assume adjacent edges are processed in alphabetical order • Number vertices in the order they are visited • Label back edges

  34. DFS and Maze Traversal • The DFS algorithm is similar to a classic strategy for exploring a maze • We mark each intersection, corner and dead end (vertex) visited • We mark each corridor (edge ) traversed • We keep track of the path back to the entrance (start vertex) by means of a rope (recursion stack) Depth-First Search

  35. Property 1 DFS(G, v) visits all the vertices and edges in the connected component of v Property 2 The discovery edges labeled by DFS(G, v) form a spanning tree of the connected component of v A B D E C Properties of DFS Depth-First Search

  36. Setting/getting a vertex/edge label takes O(1) time Each vertex is labeled twice once as UNEXPLORED once as VISITED Each edge is labeled twice once as UNEXPLORED once as DISCOVERY or BACK Method incidentEdges is called once for each vertex DFS runs in O(n + m) time provided the graph is represented by the adjacency list structure Recall that Sv deg(v)= 2m Analysis of DFS Depth-First Search

  37. Path Finding • We can specialize the DFS algorithm to find a path between two given vertices u and z using the template method pattern • We call DFS(G, u) with u as the start vertex • We use a stack S to keep track of the path between the start vertex and the current vertex • As soon as destination vertex z is encountered, we return the path as the contents of the stack AlgorithmpathDFS(G, v, z) setLabel(v, VISITED) S.push(v) if v= z return S.elements() for all e  G.incidentEdges(v) ifgetLabel(e) = UNEXPLORED w opposite(v,e) if getLabel(w) = UNEXPLORED setLabel(e, DISCOVERY) S.push(e) pathDFS(G, w, z) S.pop(e) else setLabel(e, BACK) S.pop(v) Depth-First Search

  38. Path Finding in Java Depth-First Search

  39. Cycle Finding AlgorithmcycleDFS(G, v, z) setLabel(v, VISITED) S.push(v) for all e  G.incidentEdges(v) ifgetLabel(e) = UNEXPLORED w opposite(v,e) S.push(e) if getLabel(w) = UNEXPLORED setLabel(e, DISCOVERY) pathDFS(G, w, z) S.pop(e) else T new empty stack repeat o S.pop() T.push(o) until o= w return T.elements() S.pop(v) • We can specialize the DFS algorithm to find a simple cycle using the template method pattern • We use a stack S to keep track of the path between the start vertex and the current vertex • As soon as a back edge (v, w) is encountered, we return the cycle as the portion of the stack from the top to vertex w Depth-First Search

  40. DFS for an Entire Graph • The algorithm uses a mechanism for setting and getting “labels” of vertices and edges AlgorithmDFS(G, v) Inputgraph G and a start vertex v of G Outputlabeling of the edges of G in the connected component of v as discovery edges and back edges setLabel(v, VISITED) for all e  G.incidentEdges(v) ifgetLabel(e) = UNEXPLORED w opposite(v,e) if getLabel(w) = UNEXPLORED setLabel(e, DISCOVERY) DFS(G, w) else setLabel(e, BACK) AlgorithmDFS(G) Inputgraph G Outputlabeling of the edges of G as discovery edges and back edges for all u  G.vertices() setLabel(u, UNEXPLORED) for all e  G.edges() setLabel(e, UNEXPLORED) for all v  G.vertices() ifgetLabel(v) = UNEXPLORED DFS(G, v) Depth-First Search

  41. L0 A L1 B C D L2 E F Breadth-First Search Presentation for use with the textbook Data Structures and Algorithms in Java, 6th edition, by M. T. Goodrich, R. Tamassia, and M. H. Goldwasser, Wiley, 2014 Breadth-First Search

  42. Breadth-first search (BFS) is a general technique for traversing a graph A BFS traversal of a graph G Visits all the vertices and edges of G Determines whether G is connected Computes the connected components of G Computes a spanning forest of G BFS on a graph with n vertices and m edges takes O(n + m ) time BFS can be further extended to solve other graph problems Find and report a path with the minimum number of edges between two given vertices Find a simple cycle, if there is one Breadth-First Search Breadth-First Search

  43. BFS Algorithm AlgorithmBFS(G, s) L0new empty sequence L0.addLast(s) setLabel(s, VISITED) i 0 while Li.isEmpty() Li +1new empty sequence for all v Li.elements() for all e G.incidentEdges(v) ifgetLabel(e) = UNEXPLORED w opposite(v,e) if getLabel(w) = UNEXPLORED setLabel(e, DISCOVERY) setLabel(w, VISITED) Li +1.addLast(w) else setLabel(e, CROSS) i i+1 • The algorithm uses a mechanism for setting and getting “labels” of vertices and edges AlgorithmBFS(G) Inputgraph G Outputlabeling of the edges and partition of the vertices of G for all u  G.vertices() setLabel(u, UNEXPLORED) for all e  G.edges() setLabel(e, UNEXPLORED) for all v  G.vertices() ifgetLabel(v) = UNEXPLORED BFS(G, v) Breadth-First Search

  44. Java Implementation Breadth-First Search

  45. L0 A L1 B C D E F Example unexplored vertex A visited vertex A unexplored edge discovery edge cross edge L0 L0 A A L1 L1 B C D B C D E F E F Breadth-First Search

  46. L0 L0 A A L1 L1 B C D B C D L2 E F E F L0 L0 A A L1 L1 B C D B C D L2 L2 E F E F Example (cont.) Breadth-First Search

  47. L0 L0 A A L1 L1 B C D B C D L2 L2 E F E F Example (cont.) L0 A L1 B C D L2 E F Breadth-First Search

  48. Exercise BFS Algorithm A B C D E F • Perform DFS of the following graph, start from vertex A • Assume adjacent edges are processed in alphabetical order • Number vertices in the order they are visited and note the level they are in • Label back edges

  49. Notation Gs: connected component of s Property 1 BFS(G, s) visits all the vertices and edges of Gs Property 2 The discovery edges labeled by BFS(G, s) form a spanning tree Ts of Gs Property 3 For each vertex v in Li The path of Ts from s to v has i edges Every path from s to v in Gshas at least i edges Properties of BFS A B C D E F L0 A L1 B C D L2 E F Breadth-First Search

  50. Setting/getting a vertex/edge label takes O(1) time Each vertex is labeled twice once as UNEXPLORED once as VISITED Each edge is labeled twice once as UNEXPLORED once as DISCOVERY or CROSS Each vertex is inserted once into a sequence Li Method incidentEdges is called once for each vertex BFS runs in O(n + m) time provided the graph is represented by the adjacency list structure Recall that Sv deg(v)= 2m Analysis Breadth-First Search

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