Revise README and enhance graph algorithms for Python 3

[mebriki]

This pull request modernizes and refines the graph theory codebase, providing clearer structure, robust implementations for key algorithms, and comprehensive testing. The main changes include significant improvements to both the weighted_graph.py and unweighted_graph.py modules, a complete rewrite of the documentation in README.md, and the addition of automated tests.

Key changes by theme:

Documentation and Project Structure

• Rewrote README.md to provide a detailed project overview, usage examples, visualization guides, and notes on edge cases and exceptions. The documentation now clearly distinguishes between weighted and unweighted graph modules and explains the test suite.

Testing

• Added tests/test_graphs.py with comprehensive unit tests covering shortest path algorithms, minimum spanning trees (Prim and Kruskal), cycle detection, and edge cases for both weighted and unweighted graphs.

Unweighted Graph Improvements (unweighted_graph.py)

• Rewrote isCyclic to use a depth-first search with recursion and stack tracking, making cycle detection for directed graphs more robust and readable.
• Refactored bestPath to use a standard breadth-first search (BFS), improving correctness and clarity for finding the shortest path between nodes.

Weighted Graph Improvements (weighted_graph.py)

• Rewrote isCyclic to use an undirected DFS approach, ensuring accurate cycle detection for undirected graphs.
• Reimplemented Kruskal's and Prim's algorithms for minimum spanning trees with standard, efficient approaches, including union-find for Kruskal and a priority queue for Prim. These changes increase correctness, efficiency, and handle disconnected graphs gracefully.
• Refactored Dijkstra's and Bellman-Ford algorithms to use standard implementations with proper error handling (e.g., negative weights/cycles), improved path reconstruction, and clearer logic.

General Codebase Enhancements

• Added necessary imports (e.g., heapq, deque) and improved code style for readability and maintainability. [1] [2]

These updates make the codebase more reliable, easier to use and test, and better suited for both learning and practical experimentation with graph algorithms.