Let S be a finite set. A binary relation on S is simply a collection R of ordered pairs(x,y)S×S. . For instance, S might be a set of people, and each such pair (x,y)R might mean “ x knows y ”.

An equivalence relationis a binary relation which satisfies three properties:

  • Reflexivity: localid="1659006645990" (x,y)R for all XS
  • Symmetry: If (x,y)R then (y,x)R
  • Transitivity: if (x,y)R and (y,z)R then localid="1659006784500" (x,Z)R

For instance, the binary relation “has the same birthday as” is an equivalence relation, whereas “is the father of” is not, since it violates all three properties.

Show that an equivalence relation partition set S into disjoint groups S1,S2,,Sk (in other words, S=S1S2SkandSiSj=ϕforallij ) such that:

  • Any two members of a group are related, that is, (x,y)R for any localid="1659006702579" (x,y)Si, for any i .
  • Members of different groups are not related, that is, for all ij, for all localid="1659006762355" xSi andySi, we have (x,Z)R.

(Hint: Represent an equivalence relation by an undirected graph.)

Short Answer

Expert verified

It can be shown that equivalence relation partitions set S into disjoint groups by the connected and disconnected branches.

Step by step solution

01

Explain the Equivalence relation

A relation is said to be in equivalence only if the relation satisfies reflexive, symmetry, and transitive properties.

02

Show that equivalence relation partitions set into disjoint groups.

Consider a set S that has the partitions of an undirected graph. Consider any tow vertices x and y in the undirected graph.

In an undirected graph, the relation between the two vertices are equivalent to the binary equivalence(x,y)Rfor anyx,ySi, for any i .Each connected branch in the graph is the equivalence class.

Obviously, Each connected graph is disjoint and all vertices are connected and each connected branch is disconnected from each other.

Therefore, anequivalence relation can partition set S into disjoint groups S1,S2,,Sk..

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Most popular questions from this chapter

For each node in an undirected graph, let twodegreeube the sum of the degrees of’s neighbors. Show how to compute the entire array of two degree. values in linear time, given a graph in adjacency list format

Either prove or give a counterexample: if {u,v}is an edge in an undirected graph, and during depth-first search (u)<post (v), then vis an ancestor of uin the DFS tree.

Question:Undirected vs. directed connectivity.

(a) Prove that in any connected undirected graph G =(V , E)there is a vertexvV whose removal leaves G connected. (Hint: Consider the DFS search tree for G.)

(b) Give an example of a strongly connected directed graph G(V ,E)such that, for everyvV, removing v from G leaves a directed graph that is not strongly connected.

(c) In an undirected graph with two connected components it is always possible to make the graph connected by adding only one edge. Give an example of a directed graph with two strongly connected components 0 such that no addition of one edge can make the graph strongly connected.

Run the strongly connected components algorithm on the following directed graphs G. When doing DFS on GR: whenever there is a choice of vertices to explore, always pick the one that is alphabetically first.

In each case answer the following questions.

(a) In what order are the strongly connected components (SCCs) found?

(b) Which are source SCCs and which are sink SCCs?

(c) Draw the “metagraph” (each meta-node is an SCC of G).

(d) What is the minimum number of edges you must add to this graph to make it strongly connected

Infinite paths.Let G=(V,E) be a directed graph with a designated “start vertex” sV,asetVGV, a set of “good” vertices, and a set VBV of “bad” vertices. An infinite trace of is an infinite sequence of vertices viV such that (1)v0=s, and (2) for all i0, (vi,vi+1)E. That is, p is an infinite path in G starting at vertex s. Since the setV of vertices is finite, every infinite trace of Gmust visit some vertices infinitely often.

  1. If p is an infinite trace, let Inf(p)V be the set of vertices that occur infinitely often in p. Show that Inf(p) is a subset of a strongly connected component of G.
  2. Describe an algorithm that determines if role="math" G has an infinite trace.
  3. Describe an algorithm that determines if G has an infinite trace that visits some good vertex in VG infinitely often.
  4. Describe an algorithm that determines if role="math" localid="1659627728759" G has an infinite trace that visits some good vertex in VG infinitely often, but visits no bad vertex in VB infinitely often.
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