What Is Weighted Tree in Data Structure?


Angela Bailey

A weighted tree is a specialized data structure that is used to represent hierarchical relationships between elements, where each element has an associated weight. In this article, we will explore what a weighted tree is and how it can be implemented in the context of data structures.

What is a Tree?

Before we dive into the concept of a weighted tree, let’s first understand what a tree is. In computer science, a tree is an abstract data type that represents a hierarchical structure.

It consists of nodes connected by edges, where each node can have zero or more child nodes. The topmost node in the tree is called the root node.

Tree Terminology

To better comprehend the concept of a weighted tree, it’s important to familiarize ourselves with some common terminology associated with trees:

  • Node: A fundamental unit of a tree that contains data and references to its child nodes.
  • Edge: A connection between two nodes.
  • Root: The topmost node in the hierarchy.
  • Parent: A node that has one or more child nodes.
  • Child: A node that has a parent node.
  • Sibling: Nodes that share the same parent.
  • Leaf: A node that does not have any child nodes.

The Concept of Weighted Trees

Now that we have a basic understanding of trees, let’s introduce the concept of weight in trees. In a weighted tree, each node is assigned an additional value called weight.

This weight represents some kind of numerical or qualitative property associated with the node. It could be the cost, importance, or any other relevant attribute of the node.

Application of Weighted Trees

Weighted trees find applications in various domains, including:

  • Network Routing: Weighted trees can be used to represent network topologies and find optimal routes based on weights assigned to nodes.
  • Decision Making: Weighted trees can assist in decision-making processes by assigning weights to different criteria and evaluating the best course of action.
  • Hierarchical Clustering: Weighted trees are used in clustering algorithms to group similar objects based on their associated weights.

Implementing Weighted Trees

To implement a weighted tree, we can use various data structures such as arrays, linked lists, or object-oriented programming constructs. One common approach is to create a class or struct representing the nodes of the tree. Each node would contain data and weight attributes along with references to its child nodes.

Example Code:

class Node {
  constructor(data, weight) {
    this.data = data;
    this.weight = weight;
    this.children = [];

// Creating a weighted tree
let root = new Node("A", 10);
let child1 = new Node("B", 5);
let child2 = new Node("C", 8);


In the above example, we create a simple weighted tree with three nodes: A (root), B (child of A), and C (another child of A). Each node has an associated weight assigned during initialization.


Weighted trees provide a powerful way to represent hierarchical relationships with associated weights. They find applications in various domains and can be implemented using different data structures. Understanding the concept of weighted trees is crucial for solving complex problems and optimizing decision-making processes.

With this comprehensive guide, you now have a solid understanding of what a weighted tree is and how it can be implemented. Armed with this knowledge, you can explore further applications and dive deeper into the world of data structures.

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