# Properties of relation: Reflexivity, Symmetricity and more

Properties of relations is important to understand the characteristics or the behavior of relation. Many set properties are similar for crisp relations and fuzzy relations. We have already discussed properties of crisp sets and properties of fuzzy sets. In this article, we will learn about the properties of relation

**Note**: The Greek letter chi (*chi*) is represented as χ in the article.

## Properties of relation:

### Reflexivity:

Relation R is **reflexive **if every element in set is associated with it self, i.e. μ_{R}(x, x) = 1, ∀x ∈ X

**Example:**

Consider the universe X = {1, 2, 3}. The fuzzy relation R defined over X is,

As we can see that μ_{R}(1, 1) = μ_{R}(2, 2) = μ_{R}(3, 3) = 1.

This relation holds the condition μ_{R}(x, x) = 1 for all x in X. So this is reflexive relation.

### Anti-reflexivity:

Relation R is an **anti-reflexive **if ∀x ∈ X, (x, x) ∉ R, i.e. μ_{R}(x, x) = 0, ∀x ∈ X

**Example:**

Consider the universe X = {1, 2, 3}. The fuzzy relation R defined over X is,

As we can see that μ_{R}(1, 1) = μ_{R}(2, 2) = μ_{R}(3, 3) = 0.

This relation holds the condition μ_{R}(x, x) = 0 for all x in X. So this is anti-reflexive relation.

### Symmetricity:

Relation R is called **symmetric **if element x is related to element y then element y must be related to x, i.e. μ_{R}(x, y) = μ_{R}(y, x), ∀x, y ∈ X

**Example:**

Consider the universe X = {1, 2, 3}. The fuzzy relation R defined over X is,

As we can see that the transpose of relation matrix R is the matrix it self. So the given relation is symmetric realtion.

### Anti-symmetricity:

Relation R is called **anti-symmetric **if, μ_{R}(x, y) > 0, then μ_{R}(y, x) = 0, x, y ∈ X, x ≠ y

**Example:**

Consider the universe X = {1, 2, 3}. The fuzzy relation R defined over X is,

As we can observe in above relation matrix, R(1, 3), R(2, 1) and R(3, 2) are non zero, and their corresponding R(y, x), i.e. R(3, 1), R(1, 2) and R(2, 3) are zero and hence the relation is anti-symmetric.

### Transitivity:

**For crisp Relation:**

Crisp Relation R is called **transitive** if x is related to y and y is related to z then x must be related to z.

That states if, (x, y) ∈ R and (y, z) ∈ R ⇒ (x, z) ∈ R

**For fuzzy relation:**

Let λ_{1}= μ_{R}(x_{i}, x_{j} )*, *λ_{2}= μ_{R}(x_{j}, x_{k})* *and λ = μ_{R}(x_{i}, x_{k})

For fuzzy relation to be transitive, λ ≥ min(λ_{1}, λ_{2} ) for all λ

**Note: **It is clear that ≤ is reflexive, anti-symmetric and transitive, < is anti-reflexive, anti-symmetric and transitive.

**How to test transitivity?**

For any given matrix, it is hard to manually test all the pair for different λ values. It takes tremendous amount of time. Can we do it better way? Of course, there is a smarter and easier way to test if given relation is transitive or not.

We can compute R^{2} by taking composition of relation R with it self, i.e. R^{2}= R ∘ R

μ_{R}^{2}(x, z) = max( μ_{R}(x, y), μ_{R}(y, z) ) where y ∈ X

R is transitive if R^{2} ⊂ R, i.e. μ_{R}^{2}(x, y) ≤ μ_{R}(x, y)

**Example:**

Check if given relation is transitive or not?

**Solution:**

As discussed above, to check the transitivity of relation R, we shall compute R^{2}

For the above matrices,

μ_{R}^{2}(1, 1) ≤ μ_{R}(1, 1)

μ_{R}^{2}(1, 2) ≤ μ_{R}(1, 2)

μ_{R}^{2}(1, 3) ≤/ μ_{R}(1, 3) ( ≤/ indicates not less than or equal to)

As μ_{R}^{2}(x, y) is not always less than or equal to μ_{R}(x, y), for all (x, y), hence R is not transitive

### Similarity / Equivalence Relation:

If relation R ̅ is reflexive, symmetric and transitive than it is called similarity (**equivalence**) relation

### Anti-Similarity / Partial Order Relation:

If R is similarity relation then its complement is anti-similarity (**partial order**) relation

i.e. μ_{R}‘(x, y)=1 – μ_{R}(x, y)

Anti-similarity relation is anti-reflexive, symmetric and transitive, in the sense of max-min

μ_{R}‘(x, y) ≥ min( max( μ_{R}‘(x, y), μ_{R}‘(y, z)) ) where y ∈ X

**Example:**

Check if given relation is anti-similarity relation or not?

μ_{R}‘(x, y)=1 – μ_{R}(x, y)

As it is anti-reflexive, symmetric and transitive, so R is anti-similarity relation

## Watch on YouTube: Properties of relation

### Weak Similarity / Tolerance Relation:

If relation R is reflexive and symmetric but not transitive than it is called weak similarity (**tolerance**) relation

### Order Relations:

Relation R is **order relation **if it is transitive relation

Relation R is **pre-order relation **if it is reflexive and transitive relation

Relation R is **half-order relation **if it is reflexive and weak anti-symmetric relation

### Tolerance to Equivalence relation conversion:

Tolerance (weak similarity) relation can be converted to equivalence (similarity) by taking composition of relation with itself

R′→ R ° R

R′′→ R′ ° R′

**Example – 1:**

Check if given crisp relation is equivalence or not:

**Solution:**

Here, (x_{i}, x_{i} ) ∈ R, ∀i, so R is reflexive

R = R^{T}, so R is symmetric

χ(x_{1}, x_{2}) = 1, χ(x_{2}, x_{5}) = 1 but χ(x_{1}, x_{5}) = 0, so R is not transitive

Hence R is **Not equivalence **but it is **Tolerance**

**Example 2:** χ

Convert following relation into equivalence if it is not already

**Solution:**

μ_{R}(x_{i}, x_{i}) = 1, ∀i, so relation is reflexive

μ_{R}(x_{i}, x_{j}) = μ_{R}(x_{j}, x_{i}) , so relation is symmetric

λ_{1} = μ_{R}(x_{1}, x_{2}) = 0.8

λ_{2} = μ_{R}(x_{2}, x_{5}) = 0.9

λ = μ_{R}(x_{1}, x_{5}) = 0.2

For fuzzy relation to be transitive, λ ≥ min(λ_{1}, λ_{2})

For given relation matrix λ < min( λ_{1}, λ_{2} ), so given relation is **tolerance **but **not equivalence**

We can convert tolerance relation into equivalence relation by taking composition of relation with it self. This process is to be repeated until it becomes equivalence.

λ_{1} = μ_{R}(x_{1}, x_{2}) = 0.8

λ_{2} = μ_{R}(x_{2}, x_{4}) = 0.5

λ = μ_{R}(x_{1}, x_{4}) = 0.2

For given relation matrix λ < min( λ_{1}, λ_{2} ), so given relation is yet **not equivalence**

Let us repeat the step again and take composition once again.

λ_{1} = μ_{R}(x_{1}, x_{3}) = 0.4

λ_{2} = μ_{R}(x_{3}, x_{2}) = 0.4

λ = μ_{R}(x_{1}, x_{2}) = 0.8

For given relation matrix λ > min( λ_{1}, λ_{2} ), and this is true for any pair in the above matrix. So given relation is now **equivalence**

Thus, this article concludes the all the properties of relation.

## Test Your Knowledge !

Given the relation matrix,

Answer the following questions:

- Is relation reflexive? Why?
- Is relation symmetric? Why?
- Is relation transitive? Why?
- Is relation similarity / equivalence? Why?
- Is relation partial order / anti-similar? Why?
- Is relation weak similar / tolerance? Why?

**Please post your answer / query / feedback in comment section below !**