CSE 2331/5331. Topic 9: Basic Graph Alg. Representations Basic traversal algorithms Topological sort CSE 2331/5331

Transcription

1 Topic 9: Basic Graph Alg. Representations Basic traversal algorithms Topological sort

2 What Is A Graph Graph G = (V, E) V: set of nodes E: set of edges a b c Example: d e f V ={ a, b, c, d, e, f } E ={(a, b), (a, d), (a, e), (b, c), (b, d), (b, e), (c, e), (e,f)}

3 Un-directed graph EE VV 2 Directed graph EE VV 2 a b c d e f

4 Un-directed graphs

5 Vertex Degree deg(v) = degree of v = # edges incident on v a b c d e f Lemma: vvii VV(GG) deg vv ii = 2 EE

6 Some Special Graphs Complete graph Path Cycle Planar graph Tree

7 Representations of Graphs Adjacency lists Each vertex u has a list, recording its neighbors i.e., all v s such that (u, v) E An array of V lists V[i].degree = size of adj list for node vv ii V[i].AdjList = adjacency list for node vv ii

8 Adjacency Lists For vertex v V, its adjacency list has size: deg(v) decide whether (v, u) E or not in time Θ (deg(v)) Size of data structure (space complexity): Θ( V + E ) = Θ (V+E)

9 Adjacency Matrix VV VV matrix AA AA ii, jj = 1 if vv ii, vv jj is an edge Otherwise, AA ii, jj = 0

10 Adjacency Matrix Size of data structure: Θ ( V V) Time to determine if (v, u) E : Θ(1) Though larger, it is simpler compared to adjacency list.

11 Sample Graph Algorithm Input: Graph G represented by adjacency lists Running time: Θ(V + E)

12 Connectivity A path in a graph is a sequence of vertices uu 1, uu 2,, uu kk such that there is an edge uu ii, uu ii+1 between any two adjacent vertices in the sequence Two vertices uu, ww VV GG are connected if there is a path in G from u to w. We also say that w is reachable from u. A graph G is connected if every pair of nodes uu, ww VV GG are connected.

13 Connectivity Checking How to check if the graph is connected? One approach: Graph traversal BFS: breadth-first search DFS: depth-first search

14 BFS: Breadth-first search Input: Given graph G = (V, E), and a source node s V Output: Will visit all nodes in V reachable from s For each vv VV, output a value vv. dd vv. dd = distance (smallest # of edges) from s to v. vv. dd = if v is not reachable from s.

15 Intuition Starting from source node s, Spread a wavefront to visit other nodes First visit all nodes one edge away from s Then all nodes two edges away from s Need a data-structure to store nodes to be explored.

16 Intuition cont. A node can be: un-discovered discovered, but not explored explored (finished) vv. dd : is set when node v is first discovered. Need a data structure to store discovered but unexplored nodes FIFO! Queue

17 Pseudo-code Use adjacency list representation Time complexity: Θ(V+E)

18 Correctness of Algorithm A node not reachable from s will not be visited A node reachable from s will be visited vv. dd computed is correct: Intuitively, if all nodes k distance away from s are in level k, and no other nodes are in level k, Then all nodes (k+1)-distance away from s must be in level (k+1). Rigorous proof by induction

19 BFS tree A node vv is the parent of uu if uu was first discovered when exploring vv A BFS tree TT Root: source node ss Nodes in level kk of TT are distance kk away from ss

20 Connectivity-Checking Time complexity: Θ(V+E)

21 Summary for BFS Starting from source node s, visits remaining nodes of graph from small distance to large distance This is one way to traverse an input graph With some special property where nodes are visited in non-decreasing distance to the source node s. Return distance between s to any reachable node in time Θ ( V + E )

22 DFS: Depth-First Search Another graph traversal algorithm BFS: Go as broad as possible in the algorithm DFS: Go as deep as possible in the algorithm

23 Example Perform DFS starting from vv 1 What if we add edge vv 1, vv 8

24 DFS Time complexity Θ(V+E)

25 Depth First Search Tree If v is discovered when exploring u Set vv. pppppppppppp = uu The collection of edges vv. pppppppppppp, vv form a tree, called Depth-first search tree.

26 DFS with DFS Tree

27 Example Perform DFS starting from vv 1

28 Another Connectivity Test

29 Traverse Entire Graph

30 Remarks DFS(G, k) Another way to compute all nodes reachable to the node vv kk Same time complexity as BFS There are nice properties of DFS and DFS tree that we are not reviewing in this class.

31 Directed Graphs

32 Un-directed graph EE VV 2 Directed graph Each edge uu, vv is directed from u to v EE VV 2 a b c d e f

33 Vertex Degree indeg(v) = # edges of the form uu, vv outdeg(v) = # edges of the form vv, uu a b c d e f Lemma: vvii VV(GG) indeg vv ii = EE Lemma: vvii VV(GG) outdeg vv ii = EE

34 Representations of Graphs Adjacency lists Each vertex u has a list, recording its neighbors i.e., all v s such that (u, v) E An array of V lists V[i].degree = size of adj list for node vv ii V[i].AdjList = adjacency list for node vv ii

35 Adjacency Lists For vertex v V, its adjacency list has size: outdeg(v) decide whether (v, u) E or not in time O(outdeg(v)) Size of data structure (space complexity): Θ(V+E)

36 Adjacency Matrix VV VV matrix AA AA ii, jj = 1 if vv ii, vv jj is an edge Otherwise, AA ii, jj = 0

37 Adjacency Matrix Size of data structure: Θ ( V V) Time to determine if (v, u) E : O(1) Though larger, it is simpler compared to adjacency list.

38 Sample Graph Algorithm Input: Directed graph G represented by adjacency list Running time: O(V + E)

39 Connectivity A path in a graph is a sequence of vertices uu 1, uu 2,, uu kk such that there is an edge uu ii, uu ii+1 between any two adjacent vertices in the sequence Given two vertices uu, ww VV GG, we say that w is reachable from u if there is a path in G from u to w. Note: w is reachable from u DOES NOT necessarily mean that u is reachable from w.

40 Reachability Test How many (or which) vertices are reachable from a source node, say vv 1?

41 BFS and DFS The algorithms for BFS and DFS remain the same Each edge is now understood as a directed edge BFS(V,E, s) : visits all nodes reachable from s in non-decreasing order

42 BFS Starting from source node s, Spread a wavefront to visit other nodes First visit all nodes one edge away from s Then all nodes two edges away from s

43 Pseudo-code Use adjacency list representation Time complexity: Θ(V+E)

44 Number of Reachable Nodes Compute # nodes reachable from vv kk Time complexity: Θ(V+E)

45 DFS: Depth-First Search Similarly, DFS remains the same Each edge is now a directed edge If we start with all nodes unvisited, Then DFS(G, kk) visits all nodes reachable to node vv kk BFS from previous NumReachable() procedure can be replaced with DFS.

46 More Example Is vv 1 reachable from vv 12? Is vv 12 reachable from vv 1?

47 DFS(G, k); DFS above can be replaced with BFS

48 Topological Sort

49 Directed Acyclic Graph A directed cycle is a sequence uu 1, uu 2,, uu kk, uu 1 such that there is a directed edge between any two consecutive nodes. DAG: directed acyclic graph Is a directed graph with no directed cycles.

50 Topological Sort A topological sort of a DAG G = (V, E) A linear ordering A of all vertices from V If edge (u,v) E => A[u] < A[v] undershorts pants socks shoes watch belt shirt tie jacket

51 Another Example Why requires DAG? Is the sorting order unique?

52 A topological sorted order of graph G exists if and only if G is a directed acyclic graph (DAG).

53 Question How to topologically sort a given DAG? Intuition: Which node can be the first node in the topological sort order? A node with in-degree 0! After we remove this, the process can be repeated.

54 Example undershorts pants socks shoes watch belt shirt tie jacket

55

56 Topological Sort Simplified Implementation

57 Time complexity Θ(V+E) Correctness: What if the algorithm terminates before we finish visiting all nodes? Procedure TopologicalSort(G) outputs a sorted list of all nodes if and only if the input graph G is a DAG If G is not DAG, the algorithm outputs only a partial list of vertices.

58 Remarks Other topological sort algorithm by using properties of DFS

59 Last Analyzing graph algorithms Adjacency list representation or Adjacency matrix representation CSE 2331 / 5331

60 Edge-weighted un-directed graph G = (V, E) and edge weight function ww: EE RR E.g, road network, where each node is a city, and each edge is a road, and weight is the length of this road. a 1 d 3 5 b e 4 c f CSE 2331 / 5331

61 Example 1 Assume G is represented by adjacency list Q is priority-queue implemented by min-heap CSE 2331 / 5331

62 Example 2 Assume G is represented by adjacency matrix What if move line 10 to above line 8? What if move line 10 to above line 9? CSE 2331 / 5331