Chapter 6 : Graph – Part 1

1. Chapter 6 : Graph– Part 1 교수 : 이상환 강의실 : 113,118호, 324호 연구실 : 과학관 204호 E-mail : sanghwan@kookmin.ac.kr Home : http://netapp.cs.kookmin.ac.kr 면담시간 : 수업시간 전후 1시간

2. u • v Graphs • G = (V,E) • Vis thevertexset. • Vertices are also callednodesandpoints. • Eis theedgeset. • Each edge connects two different vertices. • Edges are also calledarcsand lines. • Directed edge has an orientation(u,v).

3. u v Graphs • Undirected edge has no orientation (u,v). • Undirected graph => no oriented edge. • Directed graph => every edge has an orientation.

4. 2 3 8 1 10 4 5 9 11 6 7 Undirected Graph

5. 2 3 8 1 10 4 5 9 11 6 7 Directed Graph (Digraph)

6. 2 3 8 1 10 4 5 9 11 6 7 Applications—Communication Network • Vertex = city, edge = communication link.

7. 2 4 3 8 8 1 6 10 2 4 5 4 4 3 5 9 11 5 6 7 6 7 Driving Distance/Time Map • Vertex = city, edge weight = driving distance/time.

8. 2 3 8 1 10 4 5 9 11 6 7 Street Map • Some streets are one way.

9. n = 4 n = 1 n = 2 n = 3 Complete Undirected Graph Has all possible edges.

10. Number Of Edges—Undirected Graph • Each edge is of the form (u,v), u != v. • Number of such pairs in an nvertex graph isn(n-1). • Since edge(u,v)is the same as edge(v,u),the number of edges in a completeundirected graph isn(n-1)/2. • Number of edges in an undirected graph is<= n(n-1)/2.

11. Number Of Edges—Directed Graph • Each edge is of the form (u,v), u != v. • Number of such pairs in an nvertex graph isn(n-1). • Since edge(u,v)isnotthe same as edge(v,u),the number of edges in a complete directed graph isn(n-1). • Number of edges in a directed graph is<= n(n-1).

12. 2 3 8 1 10 4 5 9 11 6 7 Vertex Degree Number of edges incident to vertex. degree(2) = 2, degree(5) = 3, degree(3) = 1

13. 8 10 9 11 Sum Of Vertex Degrees Sum of degrees =2e(eis number of edges)

14. 2 3 8 1 10 4 5 9 11 6 7 In-Degree Of A Vertex in-degree is number of incoming edges indegree(2) = 1, indegree(8) = 0

15. 2 3 8 1 10 4 5 9 11 6 7 Out-Degree Of A Vertex out-degree is number of outbound edges outdegree(2) = 1, outdegree(8) = 2

16. Sum Of In- And Out-Degrees each edge contributes 1 to the in-degree of some vertex and 1 to the out-degree of some other vertex sum of in-degrees = sum of out-degrees = e, where e is the number of edges in the digraph

17. Sample Graph Problems • Path problems. • Connectedness problems. • Spanning tree problems.

18. 2 4 3 8 8 1 6 10 2 4 5 4 4 3 5 9 11 5 6 7 6 7 Path Finding Path between 1 and 8. Path length is20.

19. 2 4 3 8 8 1 6 10 2 4 5 4 4 3 5 9 11 5 6 7 6 7 Another Path Between 1 and 8 Path length is28.

20. 2 3 8 1 10 4 5 9 11 6 7 Example Of No Path No path between 2 and9.

21. Connected Graph • Undirected graph. • There is a path between every pair of vertices.

22. 2 3 8 1 10 4 5 9 11 6 7 Example Of Not Connected

23. 2 3 8 1 10 4 5 9 11 6 7 Connected Graph Example

24. 2 3 8 1 10 4 5 9 11 6 7 Connected Components

25. Connected Component • A maximal subgraph that is connected. • Cannot add vertices and edges from original graph and retain connectedness. • A connected graph has exactly 1component.

26. 2 3 8 1 10 4 5 9 11 6 7 Not A Component

27. 2 3 8 1 10 4 5 9 11 6 7 Communication Network Each edge is a link that can be constructed (i.e., a feasible link).

28. Communication Network Problems • Is the network connected? • Can we communicate between every pair of cities? • Find the components. • Want to construct smallest number of feasible links so that resulting network is connected.

29. 2 3 8 1 10 4 5 9 11 6 7 Cycles And Connectedness Removal of an edge that is on a cycle does not affect connectedness.

30. Cycles And Connectedness 2 Connected subgraph with all vertices and minimum number of edges has no cycles. 3 8 1 10 4 5 9 11 6 7

31. Tree • Connected graph that has no cycles. • nvertex connected graph withn-1edges.

32. Spanning Tree • Subgraph that includes all vertices of the original graph. • Subgraph is a tree. • If original graph has n vertices, the spanning tree has n vertices and n-1 edges.

33. 2 4 3 8 8 1 6 10 2 4 5 4 4 3 5 9 8 11 5 6 2 7 6 7 Minimum Cost Spanning Tree • Tree cost is sum of edge weights/costs.

34. A Spanning Tree Spanning tree cost = 51. 2 4 3 8 8 1 6 10 2 4 5 4 4 3 5 9 8 11 5 6 2 7 6 7

35. Minimum Cost Spanning Tree Spanning tree cost = 41. 2 4 3 8 8 1 6 10 2 4 5 4 4 3 5 9 8 11 5 6 2 7 6 7

36. A Wireless Broadcast Tree 2 Source = 1, weights = needed power. Cost = 4+2+5+7+8+6+4+4+8+3 = 51. 4 3 8 8 1 6 10 2 4 5 4 4 3 5 9 8 11 5 6 2 7 6 7

37. Graph Representation • Adjacency Matrix • Adjacency Lists • Linked Adjacency Lists • Array Adjacency Lists

38. 1 2 3 4 5 2 1 3 2 1 3 4 4 5 5 Adjacency Matrix • 0/1 n x nmatrix, wheren =# of vertices • A(i,j) = 1 iff (i,j) is an edge 0 1 0 1 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 1 1 1 0

39. 1 2 3 4 5 2 1 0 1 0 1 0 3 2 1 0 0 0 1 1 3 0 0 0 0 1 4 4 1 0 0 0 1 5 5 0 1 1 1 0 Adjacency Matrix Properties • Diagonal entries are zero. • Adjacency matrix of an undirected graph is symmetric. • A(i,j) = A(j,i)for alli andj.

40. 1 2 3 4 5 2 1 0 0 0 1 0 3 2 1 0 0 0 1 1 3 0 0 0 0 0 4 4 0 0 0 0 1 5 5 0 1 1 0 0 Adjacency Matrix (Digraph) • Diagonal entries are zero. • Adjacency matrix of a digraph need not be symmetric.

41. Adjacency Matrix • n2bits of space • For an undirected graph, may store only lower or upper triangle (exclude diagonal). • (n-1)n/2 bits • O(n)time to find vertex degree and/or vertices adjacent to a given vertex.

42. 2 3 1 4 5 Adjacency Lists • Adjacency list for vertexiis a linear list ofvertices adjacent from vertexi. • An array ofnadjacency lists. aList[1] = (2,4) aList[2] = (1,5) aList[3] = (5) aList[4] = (5,1) aList[5] = (2,4,3)

43. 2 aList[1] 2 4 3 [2] 1 5 [3] 5 1 [4] 5 1 4 aList[5] 2 4 3 5 Linked Adjacency Lists • Each adjacency list is a chain. Array Length = n # of chain nodes = 2e (undirected graph) # of chain nodes = e (digraph)

44. 2 aList[1] 2 4 3 [2] 1 5 [3] 5 1 [4] 5 1 4 aList[5] 2 4 3 5 Array Adjacency Lists • Each adjacency list is an array list. Array Length = n # of list elements = 2e (undirected graph) # of list elements = e (digraph)

45. Weighted Graphs • Cost adjacency matrix. • C(i,j) = cost of edge (i,j) • Adjacency lists =>each list element is a pair(adjacent vertex, edge weight)

46. Number Of C++ Classes Needed • Graph representations • Adjacency Matrix • Adjacency Lists • Linked Adjacency Lists • Array Adjacency Lists • 3 representations • Graph types • Directed and undirected. • Weighted and unweighted. • 2 x 2 = 4graph types • 3 x 4 = 12 C++ classes

47. 2 3 8 1 4 5 9 10 6 7 11 Graph Search Methods A vertex u is reachable from vertex v iff there is a path from v to u.

48. 2 3 8 1 4 5 9 10 6 7 11 Graph Search Methods A search method starts at a given vertex v and visits/labels/marks every vertex that is reachable from v.

49. Graph Search Methods • Many graph problems solved using a search method. • Path from one vertex to another. • Is the graph connected? • Find a spanning tree. • Etc. • Commonly used search methods: • Depth-first search. • Breadth-first search.

50. Depth-First Search DFS(v) { Label vertex v as reached. for (each unreached vertex u adjacent from v) DFS(u); }