Search & Traversal Algorithms For Graphs

Graph traversal is a fundamental concept in computer science that involves exploring and visiting all the nodes or vertices of a graph data structure. Graphs are used to represent various real-world relationships and connections, and understanding how to traverse them is crucial for many applications.

Practically, graph traversal is used in a wide range of scenarios, such as:

  • Pathfinding and route planning: Graphs can represent transportation networks, such as roads, railways, or airline routes, and certain variations of graph traversal algorithms are used to find the shortest or most efficient paths between locations.

  • Social media networks: Graph traversal is used to analyze the connections between users, identify influential individuals, and recommend new connections.

  • Recommendation systems: Graphs can represent the relationships between products, users, and their preferences, allowing for personalized recommendations based on the user’s browsing history and the connections in the graph.

  • Web crawling and search engine optimization: Search engines use graph traversal techniques to explore and index the interconnected web pages, allowing for efficient information retrieval and ranking of search results.

  • Biological networks: Graphs can represent the interactions between genes, proteins, or other biological entities, and graph traversal is used to study and understand these complex networks.

Below are the major Graph Traversal Techniques:

Depth First Search In a Graph

Depth-First Search (DFS) is a fundamental algorithm used in graph theory and computer science to explore and traverse a graph or a tree-like data structure. The basic idea behind DFS is to start at a particular node (the root or starting node) and then explore as far as possible along each branch before coming back and trying out other paths. Read More

Breadth First Search (BFS) For a Graph

Breadth-First Search (BFS) is a graph traversal algorithm that explores all the neighboring nodes at the current depth/distance from the target node before moving on to the nodes at the next depth level. This means that the algorithm visits all the nodes which are at a distance d from the target/root node before moving on to the nodes at a distance d+1. Read More