PPT PowerPoint Presentation

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Searching in a Graph

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Representations of Graphs

• Adjacency Matrices

• Adjacency Lists

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Adjacency Matrices

Graphs G = (V, E) can be represented by adjacency matrices

G[v₁..v_{|V |}, v₁..v_{|V |}], where the rows and columns are indexed by the nodes, and the entries G[v_i, v_j] represent the edges. In the case of unlabeled graphs, the entries are just boolean values.

A B C D

A 0 1 1 1

B 1 0 0 1

C 1 0 0 1

D 1 1 1 0

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Adjacency Matrices

In case of labeled graphs, the labels themselves may be introduced into the entries.

A B C D A

10 4 1 B

15 C

9 D

Adjacency matrices require O(|V |²) space, and so they are space-efficient only when they are dense (that is, when the graphs have many edges). Time-wise, the adjacency matrices allow easy addition and deletion of edges.

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Adjacency Lists

A representation of the graph consisting of a list of nodes, with each node containing a list of its neighboring nodes.

This representation takes O(|V | + |E|) space.

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Graph Traversal

• Depth-First Traversal

• Breadth-First Traversal

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Depth-First Traversal

• algorithm dft(x)

visit(x)

FOR each y such that (x,y) is an edge DO

IF < y was not visited yet >

THEN dft(y)

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• A recursive algorithm implicitly recording a

“backtracking” path from the root to the

node currently under consideration

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•Depth first search is another way of traversing

graphs, which is closely related to preorder traversal of a tree.

•the Breath-first search tree is typically "short and

bushy", the DFS tree is typically "long and stringy".

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Breadth-First Traversal

Visit the nodes at level i before the nodes of level i+1.

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visit(start node)

queue <- start node

WHILE queue is not empty DO x <- queue

FOR each y such that (x,y) is an edge

and y has not been visited yet DO

visit(y) queue <- y END

END

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• Each vertex is clearly

• marked at most once,

• added to the list at most once (since that happens only when it's marked), and

• removed from the list at most once.

Since the time to process a vertex is proportional to the length of its adjacency list, the total time for the whole algorithm is

O(m).

• A tree T constructed by the algorithm is called a breadth first search tree.

• The traversal goes a level at a time, left to right within a level

(where a level is defined simply in terms of distance from the root of the tree).

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Breadth-First Traversal

• Every edge of G can be classified into one of three groups.

• Some edges are in T themselves.

• Some connect two vertices at the same level of T.

• The remaining ones connect two vertices on two adjacent levels. It is not possible for an edge to skip a level.

• Breadth-first search tree really is a shortest path tree starting from its root.

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Relation between BFS and DFS

bfs(G) {

list L = empty tree T = empty

choose a starting vertex x search(x)

while(L nonempty)

remove edge (v,w) from start of L

if w not yet visited {

add (v,w) to T search(w)

} }

dfs(G) {

list L = empty tree T = empty

choose a starting vertex x search(x)

while(L nonempty)

remove edge (v,w) from end of L

if w not yet visited {

add (v,w) to T search(w)

}

search(vertex v) { }

visit(v);

for each edge (v,w)

add edge (v,w) to end of L }

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Relation between BFS and DFS

Both of these search algorithms now keep a list of edges to explore; the only difference between the two is

while both algorithms adds items to the end of L,

•BFS removes them from the beginning, which results in maintaining the list as a queue

•DFS removes them from the end, maintaining the list

as a stack.

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BFS and DFS in directed graphs

The same search routines work essentially unmodified for directed graphs.

The only difference is that when exploring a vertex v, we only want to look at edges (v,w) going out of v; we ignore the other edges coming into v.

For BFS in directed graphs each edge of the graph either

•connects two vertices at the same level

•goes down exactly one level

•goes up any number of levels.

For DFS, each edge either connects an ancestor to

•a descendant

•a descendant to an ancestor

•one node to a node in a previously visited subtree.

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Searching

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Searching

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Searching

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Searching

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Searching

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Searching

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Searching

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Searching

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Searching

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End

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End