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Data Structures & Algorithms Graphs

Data Structures & Algorithms Graphs. Richard Newman based on book by R. Sedgewick and slides by S. Sahni. Definitions. G = (V,E) V is the vertex set Vertices are also called nodes E is the edge set Each edge connects two different vertices. Edges are also called arcs. u. v. u. v.

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Data Structures & Algorithms Graphs

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  1. Data Structures & Algorithms Graphs Richard Newman based on book by R. Sedgewick and slides by S. Sahni

  2. Definitions • G = (V,E) • V is the vertex set • Vertices are also called nodes • E is the edge set • Each edge connects two different vertices. • Edges are also called arcs

  3. u • v u • v Definitions • Directed edge has an orientation (u,v) • Undirected edge has no orientation {u,v} • Undirected graph => no oriented edge • Directed graph (a.k.a. digraph) => 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

  6. 2 3 8 1 10 4 5 9 11 6 7 Application: Communication NW 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. Also known as clique

  10. Number Of Edges—Undirected Graph • Each edge is of the form (u,v), u != v • Number of such pairs in an n = |V| vertex graph is n(n-1) • Since edge (u,v) is the same as edge (v,u), the number of edges in a complete undirected graph is n(n-1)/2 • Number of edges in an undirected graph is |E| <= 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 n = |V| vertex graph is n(n-1) • Since edge (u,v) is NOT the same as edge (v,u), the number of edges in a complete directed graph is n(n-1) • Number of edges in a directed graph is |E| <= 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 (e is 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 = |E| is the number of edges in the digraph

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

  18. 2 4 3 8 8 1 6 10 2 4 4 4 3 5 9 11 5 6 7 6 7 Path Finding Path between 1 and 8 Path length is 20

  19. 2 4 3 8 8 1 6 10 2 4 4 4 3 5 9 11 5 6 7 6 7 Path Finding Another path between 1 and 8 Path length is 28

  20. 2 4 3 8 8 1 6 10 2 4 4 4 3 5 9 11 5 6 7 6 7 Path Finding No path between 1 and 10

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

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

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

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

  25. 2 3 8 1 10 4 5 9 11 6 7 Connected Components

  26. 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)

  27. 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

  28. 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.

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

  30. Tree • Connected graph that has no cycles • n vertex connected graph with n-1 edges

  31. 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

  32. 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

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

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

  35. A Wireless Broadcast Tree 2 4 3 8 8 1 6 10 2 4 5 4 4 3 5 9 8 11 5 6 2 7 6 7 Source = 1, weights = needed power. Cost = 4 + 8 + 5 + 6 + 7 + 8 + 3 = 41

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

  37. 1 2 3 4 5 2 1 3 2 1 3 4 4 5 5 Adjacency Matrix • Binary (0/1) n x n matrix, • where n = # 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

  38. 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 all i and j

  39. 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.

  40. Adjacency Matrix • n2 bits of space • For an undirected graph, may store only lower or upper triangle (exclude diagonal). • Space? (n-1)n/2 bits • Time to find vertex degree and/or vertices adjacent to a given vertex? O(n)

  41. 2 3 1 4 5 Adjacency Lists Adjacency list for vertex i is a linear list of vertices adjacent from vertex i Graph is an array of n adjacency lists aList[1] = (2,4) aList[2] = (1,5) aList[3] = (5) aList[4] = (5,1) aList[5] = (2,4,3)

  42. 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)

  43. 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)

  44. Weighted Graph Representations • Cost adjacency matrix • C(i,j) = cost of edge (i,j) • Adjacency lists • Each list element is a pair • (adjacent vertex, edge weight)

  45. Paths • Simple Path • List of distinct vertices v0, v1, … , vn • Each pair of successive vertices is an edge in E (vi, vi+1) • Hamilton Path • Visit each node exactly once • Euler Path • Visit each edge exactly once

  46. Bipartite Graph • Vertex set V can be partitioned into two disjoint subsets, V0 and V1 • No two vertices in Vi have an edge between them Complete bipartite graphs

  47. 2 3 8 1 10 4 5 9 11 6 7 Hamilton Path Does this graph have a Hamilton path? Yes! 2 or 7 to 10 Very hard in general

  48. 2 3 8 1 10 4 5 9 11 6 7 Euler Path Does this graph have a Euler path? Yes! 9 to 10 Very easy in general

  49. Euler Path • Bridges of Königsberg • People wanted to walk over all bridges without crossing one twice So they asked Euler …

  50. Euler Path • Bridges of Königsberg • People wanted to walk over all bridges without crossing one twice Does this graph have a Euler path?

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