Graphs in Data Structures: Top Techniques Every Programmer Must Know!

Graphs in data structures are used to represent relationships, with nodes (vertices) denoting entities and edges indicating connections between them. Graphs can be weighted, directed, or undirected, depending on the nature of the relationships they represent. 

In this blog, we’ll cover the different types of graphs, their storage methods, and how to traverse them effectively. Understanding graph terminology is key to working with graph-based data, which has applications in networks, databases, and more.

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Core Elements of Graphs in Data Structures

Graphs in data structures model relationships between entities using nodes (vertices) and edges. They are fundamental in computing, representing complex connections in a structured way. Graphs can be categorized based on their characteristics, optimizing representation, storage, and traversal for specific applications.

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Here are the key elements of a graph:

• Nodes (Vertices): Entities in the graph, representing objects such as users, locations, or products.
• Edges: Connections between nodes that define the relationship between them. These can be directed (arcs) or undirected, indicating one-way or two-way relationships.
• Edge Weights: Optional values assigned to edges, representing costs, distances, or other metrics.
• Directed vs. Undirected: Directed graphs (digraphs) have edges that point from one node to another, whereas undirected graphs allow two-way relationships.
• Graph Type: Depending on structure, graphs can be cyclic or acyclic, connected or disconnected, and can even have loops.
• Graph Representation: Common methods for storing graphs include adjacency matrices, adjacency lists, and edge lists, each offering benefits based on the graph's size and sparsity.

Visualization of Graphs

Graphs can be analyzed visually through tools such as scatter plots, box plots, and histograms to uncover patterns and relationships within the data:

• Scatter Plot: Scatter plots visualize relationships between two continuous variables, helping identify correlations or clusters in data represented as nodes and edges.
• Box Plot: A box plot helps visualize the spread of data and identify outliers, providing insights into node relationships or distributions.
• Histograms: Histograms represent the frequency distribution of node attributes (e.g., the number of edges connected to a node), giving insights into data centrality and skewness.

Now, let’s look at some of the major types of graphs in data structures. 

Types of Graphs Explained: Which One Should You Use in 2025?

Graphs, in their various types, are used to model real-world relationships, where nodes represent entities, and edges represent the connections between them. In modern computing, understanding the different types of graphs is crucial for selecting the most efficient approach to data manipulation and analysis. 

The choice of graph type, weighted or unweighted, directed or undirected, depends mainly on the problem you're solving and the computational constraints. Graphs are implemented using data structures in different programming languages like Python, Java, C#, and C++.

1. Weighted Graph

Graphs whose edges or paths have values. All the values seen associated with the edges are called weights. Edges value can represent weight/cost/length.

Values or weights may also represent:  

• Weights Represent Costs: In network routing, weights may represent latency, bandwidth, or time delay between two nodes.
• Edge Weight Calculation: Often, the weight is a numeric value, representing distance (e.g., miles between cities), cost (e.g., transportation expenses), or time.
• Algorithm Application: Algorithms like Dijkstra's shortest path or Prim's algorithm are designed to work with weighted graphs to find the least-cost path or minimum spanning tree.

Use Case:

In a real-world scenario like GPS navigation (e.g., Google Maps), weighted graphs are used to find the shortest driving route between two locations. The weights in this case represent the distance or time taken to travel between different points. In Java or Python, libraries like NetworkX or Java's JGraphT can be used to model and process weighted graphs for such applications.

Our learners also read: 10+ Free Data Structures and Algorithms Online Courses with Certificate 2025

2. Unweighted Graph

An unweighted graph does not assign any weights to its edges. Each edge is considered equal in terms of distance or cost. These graphs are typically used for modeling simple relationships without the need for differentiation between connections.

• No Edge Value: All edges are treated equally, with no cost.
• Basic Connectivity: Used in cases where the relationship between nodes is symmetric and doesn't require a measure of intensity or weight.
• Simpler Algorithms: Algorithms like BFS (Breadth-First Search) or DFS (Depth-First Search) are particularly useful for unweighted graphs as they focus on structure and connectivity.

Use Case:

In social networks (e.g., Facebook), an unweighted graph could represent users as nodes, with edges connecting friends. Each edge would have the same significance, showing only whether a user is connected to another, without considering the strength of the relationship. In Python, libraries like NetworkX allow easy construction and manipulation of unweighted graphs for such applications.

3. Undirected Graph

In an undirected graph, edges do not have a direction. The relationship between two nodes is bidirectional, meaning that if there's a connection from node A to node B, you can traverse from B to A. These graphs often represent mutual relationships, such as friendships in social networks

• Bidirectional Connections: The relationship between nodes is reciprocal, meaning traversal in either direction is allowed.
• Symmetric Edges: There’s no specific direction for edges; they are treated equally in both directions.
• Used in Simple Network Models: Ideal for representing symmetric relationships where direction doesn’t matter, like undirected social networks or undirected communication networks.

Use Case:

Consider a simple undirected graph for a friendship network on a platform like LinkedIn. Each node represents a user, and an edge between two nodes indicates a mutual connection (friendship). The graph representation is symmetric; if user A is connected to user B, then user B is also connected to user A. In Java, one could use the JGraphT library to model and traverse such undirected graphs for tasks like network analysis.

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4. Directed Graph

A directed graph (also known as a digraph) is a graph with edges that have a direction. Each edge has a starting node and an ending node, meaning the connection from one node to another is one-way. Directed graphs are useful for modeling asymmetric relationships such as flows, dependencies, or causality, where the direction of the relationship matters.

• Directed Edges: Each edge has a direction, pointing from one node to another, denoted as an ordered pair (A -> B).
• Applications in Data Flow: Commonly used in applications where the direction of the connection is essential, like modeling web page link structures or dependency graphs in software systems.
• Used for Graph Traversal Algorithms: Algorithms like topological sorting DAGs, used in scheduling tasks or determining the order of operations in a directed acyclic graph (DAG), are applied to directed graphs.

Use Case:

In web crawling (e.g., Google Search Engine), a directed graph can represent web pages as nodes, with directed edges indicating hyperlinks between pages. The direction of the edge signifies the flow from one page to another when a user clicks a link. In Python, frameworks like Scrapy or libraries like NetworkX can create and analyze directed graphs in such use cases.

Also Read: Top 15 Data Visualization Project Ideas: For Beginners, Intermediate, and Expert Professionals

Let’s explore the sorting of graphs in data structures for effective data analysis. 

Graph Storage Made Simple: Picking the Best Structure for Your Needs

Every storage method has its pros and cons, and the right storage method is chosen based on the complexity. The two most commonly used data structures to store graphs are: 

1. Adjacency list

The adjacency list is one of the most common ways to represent graphs, especially when the graph is sparse (i.e., not all nodes are connected). Each node is stored as an index in a one-dimensional array, and its edges are stored as a list of adjacent nodes. 

This method is memory-efficient, as it only stores the edges that exist. It is often used in web applications, social networks, and other systems where connections are dynamic and frequently updated, like ReactJS applications.

• Efficient Space Usage: Adjacency lists only store the edges that exist, leading to lower memory usage compared to other representations like adjacency matrices.
• Array-Based Indexing: The adjacency list uses an array of lists, where the index represents the node, and each list contains its adjacent nodes.
• Dynamic Graph Representation: Suitable for graphs with many nodes but relatively fewer edges, which is common in applications like social networks and web scraping.

Use Case:

An adjacency list can represent the friendships between users in a React application that manages connections between users. Each user’s ID (node) can be mapped to an array of other user IDs (edges) they are connected to.

This setup is ideal for managing dynamic connections where users may frequently add or remove friends. In React, you could manage the graph using JavaScript objects or arrays, updating the adjacency list as users connect and disconnect.

2. Adjacency matrix

Here nodes are represented as the index of a two-dimensional array, followed by edges represented as non-zero values of an adjacent matrix.

The adjacency matrix is a two-dimensional array representing a graph, where rows and columns correspond to nodes. A "1" indicates an edge between nodes, and "0" indicates no connection. This representation suits dense graphs and is efficient in parallel processing environments like Docker or Kubernetes. Adjacency matrices are also used in graph neural networks (GNNs) for machine learning applications such as TensorFlow.

• Memory Usage: The adjacency matrix requires O(n^2) space, where n is the number of nodes, making it less space-efficient for sparse graphs but ideal for dense graphs with many edges.
• Ease of Access: It allows constant-time access (O(1)) to check whether an edge exists between any two nodes, making it highly efficient for some algorithms like matrix multiplication in graph-related computations.
• Implementation Simplicity: The matrix structure is straightforward in programming languages like Python, C++, or JavaScript.

Use Case:

In a machine learning model built with TensorFlow, the adjacency matrix can represent the connectivity between nodes in a graph, especially when implementing GNNs. With Kubernetes or Docker, you could scale the matrix operations in a distributed environment, processing graph data across multiple containers for faster computations. This approach is common in applications like social network analysis, where nodes represent users and edges represent interactions or relationships between them.

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How Graph Traversal Works: Ordering Nodes and Preventing Loops

Graph traversal is a method used to search nodes in a graph. The traversal of graph in data structure is used to decide the order used for node arrangement. It also searches for edges without making a loop, which means all the nodes and edges can be searched without creating a loop. 

There are two graph traversal structures:

1. DFS (Depth First Search): In-depth search method  

The DFS search begins starting from the first node and goes deeper and deeper, exploring down until the targeted node is found. If the targeted key is not found, the search path is changed to the path that was stopped exploring during the initial search, and the same procedure is repeated for that branch.

The spanning tree is produced from the result of this search. This tree method is without the loops. The total number of nodes in the stack data structure is used to implement DFS traversal.

Steps followed to implement DFS search: 

• Step 1: The stack size must accommodate all the nodes in the graph to ensure that no node is left unvisited during traversal. For an efficient depth-first search (DFS) implementation, a dynamic stack is used to manage nodes as they are visited.
• Step 2: The traversal begins from a specified starting node pushed onto the stack. This node is marked as visited, ensuring it is not revisited later in the search process.
• Step 3: From the current node, explore its adjacent nodes that have not been visited. Each unvisited node is pushed onto the stack, marking it for future exploration. This step continues until all reachable adjacent nodes are added to the stack.
• Step 4: Visiting and pushing unvisited adjacent nodes continues recursively, ensuring that all potential paths from the current node are explored. The stack grows as new nodes are discovered, keeping track of the remaining nodes to visit.
• Step 5: If no unvisited adjacent nodes are found, the algorithm backtracks by popping the stack. This ensures that the traversal proceeds through alternative paths that may have been skipped during earlier steps.
• Step 6: Continue with the traversal and backtracking process, emptying the stack by visiting and popping nodes until no more nodes are left. This iterative process ensures that every node and edge is visited.
• Step 7: Once the stack is empty, the algorithm terminates, and a spanning tree is formed by retaining only the edges that connect the visited nodes. This tree contains no cycles and represents the most efficient path from the starting node through the graph, reducing computational overhead in subsequent analysis.

Some real-world applications of DFS include:

1. Solving Puzzles with Only One Solution:
DFS is often used in solving problems like mazes, Sudoku, and other puzzles where only one valid solution exists. By exploring all possible paths recursively, DFS ensures that all potential solutions are tested until the correct one is found. 

For example, when solving a Sudoku puzzle using DFS in Python, you can implement backtracking techniques to explore possible number placements recursively. 

2. To Test if a Graph is Bipartite:
A graph is bipartite if its vertices can be divided into two disjoint sets such that no two vertices within the same set are adjacent. DFS can be applied to check bipartiteness by attempting to color the graph with two colors. 

As you traverse using DFS, you alternate the color of adjacent nodes. However, if you find two adjacent nodes of the same color, the graph is not bipartite. This approach can be implemented in AWS Lambda to scale efficiently for large graphs in cloud-based environments.

3. Topological Sorting for Scheduling Jobs:
DFS is a crucial algorithm in topological sorting, used to order tasks or jobs based on their dependencies. For example, in job scheduling systems like Azure Databricks, tasks must be executed in a specific order based on their dependencies. 

The nodes are processed so that all prerequisites (edges) are handled before a task is executed. This makes DFS an essential part of dependency management in cloud computing architectures.

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2. BFS (Breadth-First Search): Search is implemented using a queuing method

Breadth-First Search navigates a graph in a breadth motion and utilises based on the Queue to jump from one node to another, after encountering an end in the path. 

Steps followed to implement BFS search,

• Step 1: The first step in BFS is to define the queue size according to the number of nodes in the graph. This queue will hold the nodes as they are explored.
• Step 2: Select the initial node to begin the traversal, adding it to the queue. This node will serve as the starting point for the BFS algorithm.
• Step 3: As the BFS traversal proceeds, it checks adjacent nodes that haven’t been visited yet. These unvisited adjacent nodes are added to the queue for further exploration. In cloud environments like AWS or Azure, this process can be distributed to handle graph-based data for applications like recommendation systems or network traffic analysis.
• Step 4: Nodes with no further adjacent nodes to visit and are not in the queue are removed. This ensures that we only explore nodes contributing to the graph’s search process, keeping the traversal efficient.
• Step 5: The queue is processed by repeating steps 4 and 5 until no more nodes are left to explore.
• Step 6: After the queue is empty, the traversal concludes, and the graph's spanning tree is formed by removing unused edges. 

Applications of BFS are:

1. Peer-to-Peer Networks for Efficient File Sharing

BFS is widely used in peer-to-peer (P2P) networks like BitTorrent to discover and connect all nearby peers. By traversing the network level by level, BFS ensures that files are distributed efficiently across the entire network. For example, BitTorrent uses BFS to find peers and propagate file pieces quickly, optimizing download speeds and connectivity.

2. Web Crawlers for Systematic Site Indexing

Web crawlers employ BFS to explore websites starting from the homepage, visiting pages layer by layer. This level-order traversal helps crawlers index all accessible links and content methodically. For instance, search engines use BFS to ensure important pages are discovered and ranked appropriately during indexing.

3. Social Networks for Friend Recommendations

Social networking platforms like Facebook and LinkedIn use BFS to generate friend suggestions by exploring users’ connections. Starting from a user, BFS explores direct friends first, then friends-of-friends, identifying new potential connections. This technique helps social networks grow communities by recommending relevant contacts.

Also Read: 8 Astonishing Benefits of Data Visualization in 2024 Every Business Should Know

Let’s explore the basic operations of graphs in detail. 

Fundamental Operations You Must Know About Graphs

Graphs in data structures and algorithms (DSA) serve as fundamental tools for representing complex relationships between entities. These operations allow for efficient management and manipulation of nodes and edges. 

Here's a technical breakdown of some of the core functionalities:

1. Adding and Deleting Nodes and Edges

Graphs are dynamic structures where nodes and edges can be added or removed based on changing data and relationships.

• Adding Nodes:
  • Introduces new entities to the graph (e.g., new users in a social network).
  • Increases graph size and complexity; requires updating adjacency lists or matrices.
  • Creates connections between nodes (e.g., friendships or communication links).
  • May be directed (one-way) or undirected (two-way), depending on the graph type.
• Deleting Nodes:
  • Removes an entity and all its connected edges.
  • Important for data pruning or reflecting changes like account deletions.
  • Removes specific connections to update the relationship map.
  • Useful for modeling dynamic interactions like broken links or ended friendships.

Technical Note:

These operations often involve updating adjacency lists or matrices, which impacts time complexity—O(1) for adjacency lists (adding edges), O(V+E) for deletion depending on structure.

2. Graph Traversal Techniques

Traversal algorithms enable visiting nodes systematically for search, analysis, or modification.

• Depth-First Search (DFS):
  • Explores as far as possible along a branch before backtracking.
  • Uses recursion or a stack data structure.
  • Applications: Detecting cycles, pathfinding in maze-like problems, topological sorting.
• Breadth-First Search (BFS):
  • Visits nodes level-by-level starting from the source node.
  • Uses a queue to track nodes at the current level.
  • Applications: Finding shortest paths in unweighted graphs, level-order traversal.
• Other Traversal Variants:
  • Iterative DFS: Stack-based to avoid recursion limits.
  • Bidirectional BFS: Runs BFS from both source and target for faster search in large graphs.

Traversal Use Cases:

• Verifying connectivity between nodes
• Searching for specific nodes or subgraphs
• Preparing graphs for further algorithms like shortest path or clustering

3. Pattern Identification and Subgraph Detection

Pattern identification involves detecting frequently occurring structures or motifs within a graph to uncover hidden relationships.

• Motifs:
  • Small, recurring subgraph patterns (e.g., triangles indicating mutual friendships).
  • Used in social networks, biology (protein interaction), and fraud detection.
• Subgraph Isomorphism:
  • Finding instances of a smaller graph inside a larger one.
  • Computationally intensive (NP-complete), but critical in chemical compound search, network security.
• Community Detection:
  • Identifies groups of nodes with dense internal connections.
  • Methods include modularity optimization, label propagation, and spectral clustering.
• Anomaly Detection:
  • Spots unusual patterns or outliers in graph structure.
  • Useful in network intrusion detection or fraud monitoring.

Real-World Example: Social Media Network Graph

In a social media platform, users are represented as nodes and friendships as edges. Graph operations like adding/removing nodes or edges reflect user activity, while BFS and DFS help explore connections and find shortest paths. Pattern detection reveals communities and interaction trends, enhancing friend suggestions and personalized content delivery.

Also read: Top 12 Best Practices for Creating Stunning Dashboards with Data Visualization Techniques

Next, let’s look at some advanced graph operations seen in 2025.

Advanced Graph Operations and Why They Matter

As graphs grow in complexity, developers must go beyond the basics to handle real-world challenges. Understanding these advanced aspects ensures your graph algorithms are both efficient and scalable.

Graphs often represent not just relationships, but also the strength, cost, or time associated with them. These weights add complexity but allow more precise modeling of real-world systems.

1. Weighted Graphs

Graphs often represent not just relationships, but also the strength, cost, or time associated with them. These weights add complexity but allow more precise modeling of real-world systems.

• Use Cases:
  • Navigation apps like Google Maps use weighted graphs to calculate the shortest or fastest path.
  • In finance, weighted graphs represent transaction volume or risk between entities.
• Challenges:
  • Traversal must now consider the minimum cumulative weight, not just node count.
  • Basic BFS or DFS won’t work for shortest paths; you need Dijkstra’s or Bellman-Ford algorithms.

Important Note: Weights can be positive, zero, or negative (with some algorithms like Bellman-Ford handling the latter).

2. Directed vs. Undirected Graphs

Edge direction defines whether a relationship is one-way or mutual. This distinction is crucial for modeling flow, influence, and hierarchical relationships.

• Directed Graphs (Digraphs):
  • Edges point in one direction (A → B), ideal for web links, citations, workflow pipelines, and dependencies.
  • Traversal respects edge direction, affecting reachability and search logic.
• Undirected Graphs:
  • Connections go both ways (A ↔ B), used for social networks, collaborations, and undirected interactions.
  • Symmetrical relationships simplify traversal and community detection.

Real-World Example: Twitter’s follower network is directed (you can follow without being followed), while Facebook friendships form an undirected graph.

3. Graph Storage Structures

How a graph is stored influences how fast it can be queried or updated. The right choice depends on the graph’s density, mutability, and access pattern.

• Adjacency Matrix:
  • A 2D array where each cell (i, j) indicates the presence (and possibly weight) of an edge.
  • Pros: Instant edge lookup (O(1)).
  • Cons: High memory usage (O(V²)), making it inefficient for sparse graphs.
• Adjacency List:
  • Each node maintains a list of its neighbors.
  • Pros: Space-efficient (O(V + E)), ideal for sparse graphs.
  • Cons: Slower edge lookup in dense graphs.

Storage Tips:

• Use adjacency lists for large graphs with fewer connections.
• Use matrices when you need fast edge checks or work with dense graphs (like in simulations). 

4. Complexity and Optimization

When working with large-scale or real-time systems, performance becomes critical. You need to optimize based on the graph’s structure, update frequency, and query types.

• Graph Size and Density:
  • Sparse graphs (fewer edges) require different strategies than dense ones.
  • Algorithms that work well for small graphs may become unusable at scale.
• Optimization Techniques:
  • Use heuristics (e.g., A*) for pathfinding in large maps.
  • Apply pruning to reduce search space in traversal.
  • Implement lazy updates to avoid unnecessary recalculations.
• Use Cases:
  • In logistics, optimizing route graphs can save costs and reduce delays.
  • In cybersecurity, fast anomaly detection in user behavior graphs is critical for threat prevention.

Why Mastering Graph Operations Is Critical

Graphs are the foundation of countless technologies and industries. Whether you're modeling real-world systems or building intelligent algorithms, deep graph knowledge empowers your problem-solving.

• Technology Impact:
  • AI systems like Graph Neural Networks (GNNs) rely on advanced graph operations.
  • Recommendation engines (e.g., Netflix, Amazon) use graph-based collaborative filtering.
  • Biological modeling uses graphs to analyze protein interactions or genetic links.
• Career and Skill Value:
  • Graph problems are common in tech interviews.
  • Mastery shows your ability to handle non-linear, real-world data structures.
  • Critical for roles in data science, machine learning, backend systems, and cybersecurity.

Let’s explore some practical applications of graphs in data structures.

Practical Applications of Graphs in Data Structures in 2025

Graphs in data structures are integral to various applications, providing efficient solutions in diverse fields. They model complex relationships, optimize logistics, enhance social media recommendations, and enable fraud detection. In bioinformatics and e-commerce, graphs uncover insights beyond traditional data structures. Graph databases further enable powerful querying, offering deeper analysis of interconnected datasets.

• Social Media Networks: Graphs model user relationships, enabling platforms like Facebook and LinkedIn to recommend friends based on mutual connections.
• Logistics Optimization: Graphs are used to calculate optimal delivery routes, minimizing time and cost, particularly in industries like transportation and logistics.
• Bioinformatics: In genomics, graphs represent genetic data to identify relationships and patterns, aiding drug discovery and disease prediction.
• Financial Fraud Detection: Graphs help trace and detect fraudulent transactions by analyzing real-time connections between different economic entities.
• E-commerce Recommendation Systems: Graphs power recommendation algorithms by analyzing user behavior and item relationships to suggest relevant products to customers.
• Graph Databases: Systems like Neo4j and Amazon Neptune provide efficient querying of complex networks of relationships, which traditional relational databases struggle to perform.

Now, let’s see when you can use graphs for several enterprise-related data analysis tasks. 

Graphs in Action: When They Outperform Other Structures

Graphs are essential in various advanced algorithms that solve real-world problems efficiently. Dijkstra's Algorithm finds the shortest path between two nodes in weighted graphs. In contrast, Prim's Algorithm helps compute the minimum spanning tree by connecting all nodes with the least weight.

Additionally, graph databases like Neo4j and Amazon Neptune are used for storing and querying complex data, offering advantages over traditional databases for graph-based data analysis.

1. Dijkstra's Algorithm:

• Finds the shortest path between nodes in a weighted graph.
• Iteratively selects the node with the smallest tentative distance and updates neighboring nodes until the destination is reached.

Use case: GPS navigation for calculating optimal travel routes.

2. Prim's Algorithm:

• Computes the minimum spanning tree (MST) for a connected, weighted graph.
• Starts from an arbitrary node and gradually adds the nearest node to form a spanning tree with the minimum total weight.

Use case: Network design involves minimizing the cost of connecting multiple points.

3. Graph Databases:

• Specialized databases for storing and querying complex relationships using graph theory.
• Efficiently handles complex queries involving traversing nodes and edges, ideal for highly connected data.

Use case: Used by platforms like LinkedIn or Facebook for social graph data to provide real-time connections, recommendations, and relationship queries.

Let’s comprehensively discuss some of the pros and cons of graphs in data analytics. 

Pros & Cons of Graphs in Data Structures in 2025

Graphs in data structures are powerful for modeling complex relationships, offering efficient shortest-path traversal and visual representations that simplify understanding intricate connections. They are well-suited for analyzing continuous data and provide versatile solutions across various domains. 

However, handling large-scale graphs introduces computational complexity, long processing times, and challenges maintaining data consistency and concurrency, especially in distributed graph databases.

Here’s a comparative table to address the pros and cons of graphs. 

Also read: Benefits and Advantages of Big Data & Analytics in Business

Conclusion

To excel in graphs in data structures, understanding advanced techniques is key. This includes using weighted, unweighted, directed, and undirected graphs, as well as selecting the right storage methods like adjacency lists and matrices. 

Additionally, learning traversal algorithms such as DFS and BFS is crucial for efficiently working with complex datasets. These techniques are essential for top programmers in 2025. upGrad’s courses provide the skills needed to understand and implement these graph concepts, preparing you for success in the field.

If you want to learn skills to understand graphs in data structures, these additional courses from upGrad can help you succeed.

• Professional Certificate Program in Business Analytics & Consulting in association with PwC Academy
• Analyzing Patterns in Data and Storytelling
• Case Study using Tableau, Python and SQL

Curious which courses can help you gain expertise in graphs in data structures? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center.

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Reference:
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