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Graph Traversal in Data Structures: A Complete Guide

Graph Traversal in Data Structures: A Complete Guide

Graphs, with their ability to represent complex relationships between entities, have emerged as powerful tools in various fields of computer science. In this blog, we will be looking at graph traversal in data structure, its types and its applications. We will also look at comparison between graph and tree.

Table of Contents:

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What is Graph Traversal in Data Structure?

Graphs in data structures comprise data distributed among various sets of edges (paths) and vertices (nodes) that are interconnected. The graph data structure (N, E) is organized with a set of nodes and edges. It’s essential for both nodes and vertices to be limited in number.

Graphs are widely used in computer science and various real-world applications. They model relationships and connections between different entities, making them valuable for tasks such as social network analysis, route planning, and data representation.

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What are the Types of Graphs in Data Structure?

Graphs come in various types, each serving different purposes. These various graph types provide diverse ways to represent relationships and connections in different scenarios. Let’s have a look at the types of graphs.

1. Weighted Graph:

These graphs involve edges or paths with assigned values, referred to as weights. These values can signify various factors, such as the distance between two points, like finding the shortest path between workstations in an office network. They may also represent the speed of data packets in a network or the available bandwidth.

2. Unweighted Graph:

This type lacks values or weights associated with its edges by default, unless specified otherwise.

3. Undirected Graph:

In this type, a set of objects is connected, and all edges are bidirectional. Imagine it like the connection between two friends on Facebook, where both can refer, share photos, and engage in mutual communication.

4. Directed Graph:

It is also known as a digraph, this graph involves a set of connected objects with edges directed from one node to another. Picture it like the directed connections shown in the image above.

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What are the Types of Graph Traversal?

Graph traversal is a technique employed to explore nodes in a graph, determine their order, and identify edges without forming loops. Two common structures for graph traversal are DFS (Depth First Search) and BFS (Breadth-First Search).

DFS

  • DFS, a method of in-depth exploration, starts from the initial node and searches deeper until reaching the target. If the target isn’t found, it backtracks to unexplored paths, repeating the process. The result is a spanning tree without loops. 

To implement DFS, follow these steps:

  1. Define stack size based on the total number of  nodes.
  2. Choose the initial node and push it onto the stack.
  3. Visit adjacent unexplored nodes, pushing them onto the stack.
  4. Repeat until no unvisited adjacent nodes exist.
  5. Use backtracking if necessary.
  6. Empty the stack, forming the final spanning tree by removing unused edges.

Applications of DFS include solving puzzles, testing graph bipartiteness, and topological sorting for job scheduling.

BFS

  • BFS, utilizing a queuing method, navigates a graph in breadth, moving from one node to another based on the queue.

To implement BFS, follow these steps:

  1. Define a queue based on the number of nodes.
  2. Start from any node, visit it, and add it to the queue.
  3. Visit non-visited adjacent nodes in the queue.
  4. Delete nodes without edges and not in the queue.
  5. Empty the queue.
  6. Form the spanning tree after the queue is empty.

Applications of BFS include P2P networks (e.g., Bittorrent), search engine crawlers, and social networking websites.

DFS and BFS find applications in various fields, including solving puzzles, testing graph properties, and optimizing job scheduling.

BFS vs. DFS

These are some fundamental differences between DFS and BFS, and the preferred choice between BFS and DFS depends on the specific problem and requirements.

FeatureBreadth-First Search (BFS)Depth-First Search (DFS)
Traversal OrderExplores level by level.Explore as deep as possible.
Data StructureUses a queue to store and manage nodes.Uses a stack to manage nodes.
Memory UsageTypically requires more memory.Generally, it  uses less memory.
CompletenessGuarantees the shortest path in an unweighted graph.May not find the shortest path.
ImplementationMore suitable for finding the shortest path or the minimum number of steps.Often used in topological sorting and solving mazes.
ApplicationsShortest path problems, puzzle solving, and network routing.Topological sorting, maze solving, and cycle detection.
Example Use CaseFinding the shortest path in a maze.Solving puzzles, like the N-Queens problem.

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Graph Vs. Tree

This table provides a simple comparison between graphs and trees based on their fundamental characteristics.

FeatureGraphTree
DefinitionA graph is a collection of nodes and edges, where edges can have direction and weight.A tree is a type of graph that is acyclic (no cycles) and connected (every node is connected).
StructureGraphs can be cyclic or acyclic and may have multiple connected components.Trees are always acyclic and have a single connected component.
Root NodeIn a graph, there is no concept of a root node.Trees have a designated root node from which all other nodes are reachable.
Node RelationshipsNodes in a graph may have arbitrary relationships with other nodes.Nodes in a tree have a hierarchical parent-child relationship.
PathsGraphs may have multiple paths between nodes.Trees have a unique path between any two nodes.
SubgraphsGraphs can have disconnected subgraphs.Trees are a connected structure; every pair of nodes is connected by exactly one path.
Loops/CyclesGraphs can have cycles (loops) where a sequence of edges forms a closed loop.Trees are acyclic, meaning there are no loops or cycles in the structure.
UsageUsed in various applications like social networks, transportation networks, etc.Commonly used in hierarchical structures, file systems, expression trees, etc.

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What are the Applications of Graph Traversal?

Graphs have diverse applications due to their effectiveness in representing data. They serve as valuable models for various scenarios. Here, we’ll explore some instances where graphs have significance.

  • Social network graphs: These depict connections between individuals, showcasing relationships, influences, and communication patterns. An example is the Twitter graph, illustrating the follower-follow dynamic.
  • Graphs in epidemiology: In disease control, graphs play a crucial role in modeling the spread of infectious diseases. Individuals are represented as vertices, and directed edges portray the transmission of infectious diseases. Analyzing such graphs aids in comprehending and managing the spread of diseases.
  • Protein-protein interactions graphs: Proteins, the building blocks of life, interact with each other in complex ways to carry out various biological functions. Protein-protein interaction (PPI) networks capture these interactions, where proteins are nodes and edges represent their physical connections. Analyzing PPI networks provides valuable insights into cellular processes, molecular pathways, and drug discovery. This way, graphs play an important role here as well.
  • Network packet traffic graphs: The complex world of network traffic can be effectively modeled using graphs. IP addresses, the unique identifiers of devices on the internet, are represented as nodes, and edges represent the flow of data packets between them. Analyzing these graphs enables network security experts to identify potential cyberattacks, track the spread of malware, and optimize network performance.
  • Neural networks: Neurons are vertices, and synapses are the connecting edges. Neural networks help us understand brain function and how connections adapt during learning. Remarkably, the human brain boasts around 1011 neurons and nearly 1015 synapses. This intricate network provides insights into cognitive processes and learning mechanisms.

Conclusion

So, that’s the scoop on graph traversal in data structures! It’s like having a secret map to uncover cool stuff. We’ve learned about different types of graphs, like the ones with weights or without any baggage, and how to roam around using DFS and BFS. Whether you’re strolling wide with BFS or taking a deep dive with DFS, graphs aren’t just in computers—they’re shaping the cool stuff in science and beyond.

Frequently Asked Questions: FAQs

What are the traversal techniques of graphs and trees in data structures?

Traversal techniques in graphs and trees involve systematically visiting and processing each node. Common methods include depth-first and breadth-first traversals.

What is an example of traversing?

Example of traversing: In a tree, a pre-order traversal would visit the root node first, then its left and right subtrees.

What is traversal in a binary tree?

Traversal in a binary tree involves systematically visiting each node. In-order traversal visits the left subtree, then the root, and finally the right subtree. Other methods include pre-order and post-order traversals.

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About the Author

Senior Consultant Analytics & Data Science

Presenting Sahil Mattoo, a Senior Consultant Analytics & Data Science at Eli Lilly and Company is an accomplished professional with 14 years of experience across data science, analytics, and technical leadership domains, demonstrates a remarkable ability to drive business insights. Sahil holds a Post Graduate Program in Business Analytics and Business Intelligence from Great Lakes Institute of Management.