Graph Data Structure in C

Graph Data Structure in C

Graph ek advanced non-linear data structure hai jo real-world relationships ko represent karta hai. Agar aap DSA roadmap for beginners follow kar rahe hain, to Graph samajhna bahut important hai kyunki ye complex problems jaise social networks, maps aur routing systems me use hota hai.

Tree ek special type ka graph hi hota hai, lekin graph me cycles aur multiple connections ho sakte hain. Isi wajah se Graph algorithms interviews aur competitive programming me frequently pooche jate hain.

📌 What is Graph in Data Structure?

Graph ek collection hota hai vertices (nodes) aur edges (connections) ka.

👉 Important terms:

• Vertex (Node) – data point

• Edge – connection between nodes

• Degree – node ke connections ki sankhya

⚙️ Types of Graph

1. Undirected Graph

Edges dono direction me hote hain

2. Directed Graph (Digraph)

Edges ek direction me hote hain

3. Weighted Graph

Edges ke saath weight (cost) hota hai

4. Unweighted Graph

Edges ka koi weight nahi hota

🔄 Graph Representation

1. Adjacency Matrix

2D array me graph store hota hai

2. Adjacency List

Linked list ka use hota hai

👉 Adjacency list zyada efficient hoti hai

💻 Graph Representation in C (Adjacency List)

#include <iostream>

#include <vector>

using namespace std;

vector<int> graph[100];

// Add edge

void addEdge(int u, int v) {

    graph[u].push_back(v);

    graph[v].push_back(u); // undirected graph

}

// Display graph

void display(int n) {

    for (int i = 0; i < n; i++) {

        cout << i << " -> ";

        for (int x : graph[i]) {

            cout << x << " ";

        }

        cout << endl;

    }

}

int main() {

    int n = 5;

    addEdge(0,1);

    addEdge(0,2);

    addEdge(1,3);

    addEdge(2,4);

    display(n);

    return 0;

}

BFS Code

#include <queue>

#include <vector>

using namespace std;

vector<int> adj[100];

bool visited[100];

void bfs(int start) {

    queue<int> q;

    q.push(start);

    visited[start] = true;

    while (!q.empty()) {

        int node = q.front();

        q.pop();

        cout << node << " ";

        for (int neighbor : adj[node]) {

            if (!visited[neighbor]) {

                visited[neighbor] = true;

                q.push(neighbor);

            }

        }

    }

}

DFS Code

#include <vector>

using namespace std;

vector<int> adj[100];

bool visited[100];

void dfs(int node) {

    visited[node] = true;

    cout << node << " ";

    for (int neighbor : adj[node]) {

        if (!visited[neighbor]) {

            dfs(neighbor);

        }

    }

}

👉 Ye simple graph representation hai

🔁 Graph Traversal

Graph traversal ka matlab nodes ko visit karna hai:

🔵 BFS (Breadth First Search)

• Level by level traversal

• Queue ka use hota hai

🔴 DFS (Depth First Search)

• Depth me traversal

• Stack ya recursion use hota hai

💡 Example

Graph traversal me agar BFS use karein:
👉 Starting node se sabse pehle uske neighbours visit honge

DFS me:
👉 Ek path ko end tak follow kiya jata hai

📈 Time Complexity

Algorithm                     Complexity

BFS                               O(V + E)

DFS                               O(V + E)

👉 Yaha V = vertices, E = edges

🎯 Applications of Graph

• Google Maps (shortest path)

• Social networks (friends connections)

• Web crawling

• Network routing

• AI & machine learning

⚡ Advantages

• Complex relationships represent karta hai

• Flexible structure

• Real-world modeling

❌ Disadvantages

• Implementation complex

• Memory usage zyada

• Visualization difficult

🔥 Graph vs Tree

Feature                          Graph                           Tree

Cycle                             Allowed                     Not allowed

Structure                       Complex                    Hierarchical

Root                              Optional                     Mandatory

💡 Common Mistakes

• Graph representation galat choose karna

• BFS aur DFS confuse karna

• Visited nodes track na karna

👉 Tip: Diagram bana ke samjho

🚀 How to Master Graph

1. Basic concepts samjho

2. BFS aur DFS practice karo

3. Shortest path algorithms (Dijkstra) seekho

4. Problems solve karo (20+)

Agar aap Graph seekh rahe hain to ye topics bhi zaroor padhein:

• 👉 DSA Roadmap for Beginners in Hindi

• 👉 Tree in Data Structure

• 👉 Stack in Data Structure

• 👉 Queue in Data Structure

• 👉 Linked List in Data Structure

• 👉 Recursion in C

• 👉 Binary Search in Data Structure

• 👉 Graph In Dsa

👉 Ye sab topics milkar aapki DSA strong banate hain

🏁 Conclusion

Graph ek powerful aur advanced data structure hai jo complex problems ko solve karne me use hota hai. Agar aap DSA roadmap follow kar rahe hain to Graph aapka final aur sabse important step hai.

Practice aur consistency ke saath aap Graph ko master kar sakte hain aur coding interviews me strong performance de sakte hain.

Read Next : Linked List

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