This project implements three core graph algorithms using pure Python:

1. Dijkstra's Algorithm – for finding the shortest paths from a source node.
2. Kruskal's Algorithm – for computing a Minimum Spanning Tree (MST).
3. DFS with Topological Sort and Cycle Detection – for analyzing directed graphs.

. ├── inputs/ → Graph input files (auto-generated) ├── outputs/ → Algorithm results written here ├── graph_generator_custom.py → Generates random graphs with/without cycles ├── graph_solver_custom.py → Runs selected algorithm on input graphs └── README.txt → This file

✓ Requires Python 3.6+ ✓ No external libraries needed

Run: python graph create.py

This creates 10 graphs:

• 5 Undirected (for Kruskal and Dijkstra)
• 5 Directed (some cyclic, some acyclic for DFS/Topo)

Each graph file includes:

• Comment lines for metadata
• Number of vertices, edges, and graph type
• List of edges with weights
• A source node (used by Dijkstra)

Run: python main.py

You will be prompted to choose one of the options:

1. Dijkstra (SSSP)
2. Kruskal (MST)
3. DFS Topological Sort / Cycle Detection

The script will:

• Pick 5 valid input graphs randomly
• Run the selected algorithm
• Save results to the 'outputs/' folder

• Uses only Python’s built-in libraries (os, random, collections)
• Algorithms provide: • Paths and costs (Dijkstra) • MST edges and total cost (Kruskal) • Topological order or all cycles (DFS)

18 35 U A B 5 B C 2 ... A

MST Edges: A - B : 3 C - D : 2 ...

Total MST cost: 106

TEJASWI

None. Uses only:

• os
• random
• collections