Developed a modular Python framework for graph theory analysis, implementing complex algorithms like Hierholzer (Eulerian circuits) and A Search. Managed data parsing for spatial and weighted graph datasets.
- Heuristic Search: Includes A* and Best-First Search for optimized pathfinding.
- Structural Analysis: Tools to detect Directed Acyclic Graphs (DAGs) and perform Topological Sorting.
- Eulerian Theory: Implementation of Hierholzer's algorithm to find Eulerian circuits.
- Combinatorial Optimization: Greedy approximation for the Minimum Vertex Cover problem.
The project processes different graph formats: -Directed & Weighted: Graphs with edge costs and specific directions.
- Undirected & Unweighted: Basic connectivity structures.
- Spatial Data: Vertex positioning (
x, ycoordinates) for visualization purposes.
- Reads and parses edge lists from raw text files.