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Chapter 9: Graphs

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Mark Allen Weiss: Data Structures and Algorithm Analysis in Java

Chapter 9: Graphs

Spanning Trees

Lydia Sinapova, Simpson College

- Spanning Trees in Unweighted Graphs
- Minimal Spanning Trees in Weighted Graphs - Prim’s Algorithm
- Minimal Spanning Trees in Weighted Graphs - Kruskal’s Algorithm
- Animation

Spanning tree: a tree that contains all vertices in the graph.

Number of nodes: |V|

Number of edges: |V|-1

A table (an array) T

size = number of vertices,

Tv= parent of vertex v

Adjacency lists

A queue of vertices to be processed

- Choose a vertex S and store it in a queue, set
- a counter = 0 (counts the processed nodes), and Ts = 0 (to indicate the root),
- Ti = -1,i s (to indicate vertex not processed)

2. While queue not empty and counter < |V| -1 :

Read a vertex Vfrom the queue

For all adjacent vertices U :

If Tu = -1(not processed)

Tu V

counter counter + 1

store U in the queue

Result:

Root: S, such that Ts = 0

Edges in the tree: (Tv, V)

Complexity:O(|E| + |V|) - we process all edges and all nodes

- Table
- 0 // 1 is the root
- 1 // edge (1,2)
- 2 // edge (2,3)
- 1 // edge (1,4)
- // edge (2,5)
- Edge format: (Tv,V)

2

3

1

4

5

Given: Weighted graph.

Find a spanning tree with the minimal sum of the weights.

Similar to shortest paths in a weighted graphs.

Difference: we record the weight of the current edge, not the length of the path .

A table : rows = number of vertices,

three columns:

T(v,1) = weightof the edge from

parent to vertex V

T(v,2) = parent of vertex V

T(v,3) = True, if vertex V is fixed in the tree,

False otherwise

Adjacency lists

A priority queue of vertices to be processed.

Priority of each vertex is determined by the weight of edge that links the vertex to its parent.

The priority may change if we change the parent, provided the vertex is not yet fixed in the tree.

Initialization

For all V, set

first column to –1 (not included in the tree)

second column to 0 (no parent)

third column to False (not fixed in the tree)

Select a vertex S, set T(s,1) = 0 (root: no parent)

Store S in a priority queue with priority 0.

1. DeleteMinVfrom the queue, setT(v,3) = True

2. For all adjacent toVverticesW:

If T(w,3)= Truedo nothing – the vertex is fixed

If T(w,1) = -1(i.e. vertex not included)

T(w,1) weight of edge (v,w)

T(w,2) v (the parent of w)

insert w in the queue with

priority = weight of (v,w)

If T(w,1) > weight of (v,w)

T(w,1) weight of edge (v,w)

T(w,2) v (the parent of w)

update priority of w in the queue

remove, and insert with new priority = weight of (v,w)

At the end of the algorithm, the tree would be represented in the table with its edges

{(v, T(v,2) ) | v = 1, 2, …|V|}

Complexity: O(|E|log(|V|))

The algorithm works with :

set of edges,

tree forests,

each vertex belongs to only one tree in the forest.

1. Build |V| trees of one vertex only - each vertex is a tree of its own. Store edges in priority queue

2. Choose an edge (u,v) with minimum weight

if uand v belong to one and the same tree,

do nothing

ifu and vbelong to different trees,

link the trees by the edge (u,v)

3. Perform step 2 until all trees are combined into one tree only

Edges in

Priority Queue:

(1,2) 1

(3,5) 2

(2,4) 3

(1,3) 4

(4,5) 4

(2,5) 5

(1,5) 6

1

1

2

4

3

5

6

4

3

2

4

5

Forest of 5 trees

1

2

3

4

5

Edges in

Priority Queue:

(1,2) 1

(3,5) 2

(2,4) 3

(1,3) 4

(4,5) 4

(2,5) 5

(1,5) 6

Process edge (1,2)

1

1

2

3

4

5

Process edge (3,5)

1

1

2

4

2

3

5

Edges in

Priority

Queue:

(2,4) 3

(1,3) 4

(4,5) 4

(2,5) 5

(1,5) 6

Process edge (2,4)

1

1

2

3

2

3

5

4

Process edge (1,3)

1

1

All trees are combined in one, the algorithm stops

2

4

3

2

3

5

4

Comparison:Are the vertices in the same tree

Union:combine two disjoint sets to form one set - the new tree

Implementation

Union/findoperations: the unions are represented as trees - nlogn complexity.

The set of trees is implemented by an array.

O(|E|log(|V|)

Explanation:

The complexity is determined by the heap operations and the union/find operations

Union/find operations on disjoint sets represented as trees: tree search operations, with complexity O(log|V|)

DeleteMin in Binary heaps with N elements is O(logN),

When performed for N elements, it is O(NlogN).

Kruskal’s algorithm works with a binary heap that contains all edges: O(|E|log(|E|))

However, a complete graph has

|E| = |V|*(|V|-1)/2, i.e. |E| = O(|V|2)

Thus for the binary heap we have

O(|E|log(|E|)) = O(|E|log (|V|2) = O(2|E|log (|V|)

= O(|E|log(|V|))

Since for each edge we perform DeleteMin operation and union/find operation, the overall complexity is:

O(|E| (log(|V|) + log(|V|)) =O(|E|log(|V|))

Sparse trees - Kruskal's algorithm :

Guided by edges

Dense trees - Prim's algorithm :

The process is limited by the number of the processed vertices