Graph data structure is a basic fundamental in computer science. It is composed of nodes and the edges which creates connection between them. As we have already covered graph data structure and all the basic graph traversal algorithms like DFS and BFS.
The edges which connects nodes in the graph maintains the entity relationship between them. In this blog we will be covering the top 5 real world applications of graph algorithms. We will also be covering the advantages, disadvantages and applications of graph data structure algorithms.
What are the graph algorithms?
Graph algorithms are the techniques to traverse the nodes in the graph. There are different graph traversal algorithms like Dijkstra’s algorithm, Breadth First Search, Depth First Search, Bellman Ford Algorithm, Floyd Warshall Algorithm, Minimum Spanning Trees, Topological Sorting and many more.
The two most commonly used traversal techniques are –
- Breadth-First Search (BFS): This explores the graph level by level. Think of it like dropping a stone into water and watching the ripples spread outward evenly. BFS visits all the immediate neighbors of a node before moving to the next level of neighbors. It is especially useful when you need the shortest path in an unweighted graph.
- Depth-First Search (DFS): In contrast DFS dives deep into a path as far as it can go before backtracking. It is more like exploring a maze by following one route to the end before trying a different one. DFS is great for tasks like detecting cycles, solving puzzles, or navigating deep hierarchical structures.
The diagram which is given below is the example of Breadth First Search graph traversal algorithm.
Starting from node A the BFS traversal order is:
A → B → C → G → D → E → F → H → I
Top 5 real world applications of graph algorithms
In this section we are exploring the real world applications of graph algorithms. Starting with the very famous application that is route planning and navigation, social media networking, web crawling and search engines, recommendation systems, network security and fraud detection.
Route Planning and Navigation (Google Maps, Uber)
Graph algorithms help many apps show the best way to travel from one place to another. Apps like Google Maps and Uber treat places as points and roads as paths between them. They use graph algorithms such as Dijkstra and A star to find the shortest or fastest route. These methods also look at live road changes and traffic. As a result drivers and passengers get reliable directions and save time during their journey.
Social Networks (Facebook, Linkedin)
Social networks rely heavily on graph algorithms to build connections and improve user experience. Platforms like Facebook and LinkedIn represent people as nodes and relationships as links between them.
Graph traversal techniques such as BFS and DFS are used to suggest friends identify communities and show how closely connected users are. For example the “People You May Know” feature on LinkedIn uses graph algorithms to find mutual contacts and suggest new connections that may be professionally beneficial.
Web Crawling and Search Engines
Search engines like Google use graph algorithms to navigate the vast world of online content. Web pages are treated as nodes and hyperlinks between them act as edges forming a massive web graph.
Graph traversal helps crawlers discover and index new pages. One of the most famous graph algorithms PageRank ranks pages based on how many and how important the links to them are. This system enables search engines to show the most relevant results when a user searches for something online.
Recommendation Systems (Amazon, Netflix, YouTube)
Graph traversal algorithms make online suggestions smarter. Sites like Amazon Netflix and Spotify treat users and items as points. They track actions such as likes views or buys to create paths between these points.
Then they use graph algorithms to find patterns and offer new content. For example Netflix suggests shows watched by people with similar tastes. This makes each user’s experience more personal and engaging.
Network Security and Fraud Detection
Graph algorithms also help protect systems from fraud and attacks. Banks and companies create graphs of users devices and actions. Then they use these algorithms to spot strange links or steps.
For example a sudden connection between far off users may be a sign of fraud. By acting early teams can stop the problem before it grows. These tools make digital spaces safer for everyone.
Advantages and Disadvantages of Graph Algorithms
In this we will be talking about the benefits of graph traversal algorithms and also the limitations part of it.
Advantages of Graph Algorithms
- They help solve tricky problems like finding the best route or connection with ease.
- We rely on them every day in apps like Google Maps or LinkedIn without even noticing.
- They make it easier to spot useful patterns in how things or people are connected.
- Even with huge amounts of data they still work fast and stay reliable.
- Many modern technologies are built on the smart logic behind graph algorithms.
Disadvantages of Graph Algorithms
- They can get slow and memory heavy when dealing with massive graphs.
- Some graph algorithms are hard to implement and understand without a strong background.
- Real world data does not always fit perfectly into a graph structure.
- Visualizing large graphs can get messy and hard to interpret.
- They sometimes need extra data cleanup or prep before they even work well.