Enhanced Graph Representation and Timing Experiment

📅 2023-09-11 — Session: Enhanced Graph Representation and Timing Experiment

🕒 19:30–20:55
🏷️ Labels: Python, Graph Theory, Error Handling, Data Visualization, Performance Measurement
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal: The session aimed to enhance the representation of graph data structures in Python and optimize the timing experiment function to measure execution times across different graph representations.

Key Activities:

• Developed Python code to plot comparative graphs for EdgeSet and NeighSet methods using Matplotlib.
• Modified a DataFrame to include sparsity and edges parameters for better graph representation analysis.
• Implemented a graph data structure using an adjacency matrix, including methods for initialization and modification.
• Updated the timing experiment function to measure execution times for edge sets, neighborhood sets, and adjacency matrices.
• Corrected errors in graph methods, including TypeErrors, ValueErrors, and IndexErrors, by adjusting code to ensure proper argument handling and method calls.
• Simplified graph initialization by generalizing method arguments and using default values.
• Addressed variable overwriting and argument conflicts in the timing_experiment function by reorganizing function parameters.

Achievements:

• Successfully plotted comparative graphs for execution time analysis.
• Enhanced graph class methods for better initialization and error handling.
• Improved the timing experiment function to handle different graph representations effectively.

Pending Tasks:

• Further testing and validation of the updated graph methods and timing experiment function to ensure robustness and accuracy.