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CS 225 Data Structures No Novem ember er 16 Gr Graph aph Im - PowerPoint PPT Presentation

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CS 225 Data Structures No Novem ember er 16 Gr Graph aph Im Implementations and Tr Traversals G G Ca Carl Evans Gr Grap aph ADT Functions: - insertVertex(K key); Data: - insertEdge(Vertex v1, Vertex v2, K key); - Vertices -

  1. CS 225 Data Structures No Novem ember er 16 – Gr Graph aph Im Implementations and Tr Traversals G G Ca Carl Evans

  2. Gr Grap aph ADT Functions: - insertVertex(K key); Data: - insertEdge(Vertex v1, Vertex v2, K key); - Vertices - Edges - removeVertex(Vertex v); - Some data structure - removeEdge(Vertex v1, Vertex v2); maintaining the structure between vertices and edges. - incidentEdges(Vertex v); - areAdjacent(Vertex v1, Vertex v2); V b d X Z h - origin(Edge e); e - destination(Edge e); W g f Y

  3. Key Ideas: Ed Edge List - Given a vertex, O(1) lookup in vertex list - Implement w/ a hash table, etc u - All basic ADT operations runs in O(m) time a c b d v w z Vertex List Edge List u u v a v v w b w u w c z w z d

  4. Key Ideas: Adjacency M Ad Matri rix - Given a vertex, O(1) lookup in vertex list u - Given a pair of vertices (an edge), a c O(1) lookup in the matrix b d v w z - Undirected graphs can use an upper triangular matrix u v w z u u v a u Ø Ø v v w b v Ø Ø w u w c w Ø z w z d z Ø

  5. Gr Grap aph Imp Implemen ementati tion: Adjac jacen ency List u a c b d v w z u v a u v w b v w u w c w z d z

  6. Ad Adjacency L List u a c b d v w z a c u v a u d=2 a b v w b v d=2 b c d w u w c d=3 w z d z d d=1

  7. Ad Adjacency L List u a c b d v w z a c u v a u d=2 a b v w b v d=2 b c d w u w c d=3 w z d z d d=1

  8. insertVertex(K key): Adjacency L Ad List u a c b d v w z a c u v a u d=2 a b v w b v d=2 b c d w u w c d=3 w z d z d d=1

  9. removeVertex(Vertex v): Adjacency L Ad List u a c b d v w z a c u v a u d=2 a b v w b v d=2 b c d w u w c d=3 w z d z d d=1

  10. incidentEdges(Vertex v): Adjacency L Ad List u a c b d v w z a c u v a u d=2 a b v w b v d=2 b c d w u w c d=3 w z d z d d=1

  11. areAdjacent(Vertex v1, Vertex v2): Adjacency L Ad List u a c b d v w z a c u v a u d=2 a b v w b v d=2 b c d w u w c d=3 w z d z d d=1

  12. insertEdge(Vertex v1, Vertex v2, K key): Adjacency L Ad List u a c b d v w z a c u v a u d=2 a b v w b v d=2 b c d w u w c d=3 w z d z d d=1

  13. Edge List Adjacency Matrix Adjacency List Expressed as O(f) Space n+m n 2 n+m insertVertex(v) 1 n 1 removeVertex(v) m n deg(v) insertEdge(v, w, k) 1 1 1 removeEdge(v, w) 1 1 1 incidentEdges(v) m n deg(v) min( deg(v), areAdjacent(v, w) m 1 deg(w) )

  14. mp_traversals and mp_mazes • mp_traversals • Vertex Set : The pixels are the vertices • Edge Set : There is an edge between every n/s/e/w pixel unless the color change exceeds the tolerance • There are several graphs here depending on the tolerance • mp_mazes • Vertex Set: The squares in the maze are the vertices • Edge Set: There is an edge between two vertices if canTravel() returns true • Once the maze is made this graph is a spanning tree of the graph with canTravel() returning true.

  15. Tr Traversal: Objective: Visit every vertex and every edge in the graph. Purpose: Search for interesting sub-structures in the graph. We’ve seen traversal before ….but it’s different: • • Ordered • • Obvious Start • •

  16. Tr Traversal: BFS A C D B E F G H

  17. Tr Traversal: BFS v d P Adjacent Edges A B A C C D B D E E F F G H G H

  18. Tr Traversal: BFS d p Adjacent Edges 0 A A C B D 1 A B A C E A 1 A C B A D E F 1 A D A C F H C D B 2 C E B C G E F 2 C F C D G 3 E G E F H G H 2 D H D G G H F E D B C A

  19. 1 BFS(G): 2 Input: Graph, G 3 Output: A labeling of the edges on 4 G as discovery and cross edges 5 6 foreach (Vertex v : G.vertices()): 7 setLabel(v, UNEXPLORED) 8 foreach (Edge e : G.edges()): 9 setLabel(e, UNEXPLORED) 10 foreach (Vertex v : G.vertices()): 11 if getLabel(v) == UNEXPLORED: 12 BFS(G, v) 14 BFS(G, v): 15 Queue q 16 setLabel(v, VISITED) 17 q.enqueue(v) 18 19 while !q.empty(): 20 v = q.dequeue() 21 foreach (Vertex w : G.adjacent(v)): 22 if getLabel(w) == UNEXPLORED: 23 setLabel(v, w, DISCOVERY) 24 setLabel(w, VISITED) 25 q.enqueue(w) 26 elseif getLabel(v, w) == UNEXPLORED: 27 setLabel(v, w, CROSS)

  20. BF BFS An S Analysis Q: Does our implementation handle disjoint graphs? If so, what code handles this? • How do we use this to count components? Q: Does our implementation detect a cycle? • How do we update our code to detect a cycle? Q: What is the running time?

  21. Ru Running t time of of BF BFS d p v Adjacent 0 A A C B D 1 A B A C E A 1 A C B A D E F C D 1 A D A C F H B 2 C E B C G E F 2 C F C D G G H 3 E G E F H While-loop at :19 ? 2 D H D G For-loop at :21 ? G H F E D B C A

  22. 1 BFS(G): 2 Input: Graph, G 3 Output: A labeling of the edges on 4 G as discovery and cross edges 5 6 foreach (Vertex v : G.vertices()): 7 setLabel(v, UNEXPLORED) 8 foreach (Edge e : G.edges()): 9 setLabel(e, UNEXPLORED) 10 foreach (Vertex v : G.vertices()): 11 if getLabel(v) == UNEXPLORED: 12 BFS(G, v) 14 BFS(G, v): 15 Queue q 16 setLabel(v, VISITED) 17 q.enqueue(v) 18 19 while !q.empty(): 20 v = q.dequeue() 21 foreach (Vertex w : G.adjacent(v)): 22 if getLabel(w) == UNEXPLORED: 23 setLabel(v, w, DISCOVERY) 24 setLabel(w, VISITED) 25 q.enqueue(w) 26 elseif getLabel(v, w) == UNEXPLORED: 27 setLabel(v, w, CROSS)

  23. BF BFS O S Observation ons d p v Adjacent 0 A A C B D Q: What is a shortest path 1 A B A C E from A to H ? 1 A C B A D E F 1 A D A C F H Q: What is a shortest path 2 C E B C G from E to H ? 2 C F C D G 3 E G E F H 2 D H D G Q: How does a cross edge relate to d ? A C D B Q: What structure is made E F from discovery edges? G H

  24. BF BFS O S Observation ons Obs. 1: Traversals can be used to count components. Obs. 2: Traversals can be used to detect cycles. Obs. 3: In BFS, d provides the shortest distance to every vertex. Obs. 4: In BFS, the endpoints of a cross edge never differ in distance, d , by more than 1: |d(u) - d(v)| = 1

  25. Tr Traversal: DFS D A B C F H E G K J

  26. 1 BFS(G): 2 Input: Graph, G 3 Output: A labeling of the edges on 4 G as discovery and cross edges 5 6 foreach (Vertex v : G.vertices()): 7 setLabel(v, UNEXPLORED) 8 foreach (Edge e : G.edges()): 9 setLabel(e, UNEXPLORED) 10 foreach (Vertex v : G.vertices()): 11 if getLabel(v) == UNEXPLORED: 12 BFS(G, v) 14 BFS(G, v): 15 Queue q 16 setLabel(v, VISITED) 17 q.enqueue(v) 18 19 while !q.empty(): 20 v = q.dequeue() 21 foreach (Vertex w : G.adjacent(v)): 22 if getLabel(w) == UNEXPLORED: 23 setLabel(v, w, DISCOVERY) 24 setLabel(w, VISITED) 25 q.enqueue(w) 26 elseif getLabel(v, w) == UNEXPLORED: 27 setLabel(v, w, CROSS)

  27. 1 DFS(G): 2 Input: Graph, G 3 Output: A labeling of the edges on 4 G as discovery and back edges 5 6 foreach (Vertex v : G.vertices()): 7 setLabel(v, UNEXPLORED) 8 foreach (Edge e : G.edges()): 9 setLabel(e, UNEXPLORED) 10 foreach (Vertex v : G.vertices()): 11 if getLabel(v) == UNEXPLORED: 12 DFS(G, v) 14 DFS(G, v): 15 Queue q 16 setLabel(v, VISITED) 17 q.enqueue(v) 18 19 while !q.empty(): 20 v = q.dequeue() 21 foreach (Vertex w : G.adjacent(v)): 22 if getLabel(w) == UNEXPLORED: 23 setLabel(v, w, DISCOVERY) 24 setLabel(w, VISITED) 25 DFS(G, w) 26 elseif getLabel(v, w) == UNEXPLORED: 27 setLabel(v, w, BACK)

  28. Running t Ru time of of D DFS D A Labeling: B C F H • Vertex: E G K J • Edge: Queries: • Vertex: • Edge:

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