📅 2025-09-12 — Session: Graph Analysis and Community Detection Enhancement
🕒 12:00–15:40
🏷️ Labels: Graph Analysis, Community Detection, Python Scripting, Data Processing, Knowledge Base
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to enhance graph analysis techniques and community detection strategies through detailed metric evaluation and Python scripting.
Key Activities
- Conducted a detailed analysis of graph structures, focusing on metrics like quantiles, support, lift, and nPMI to inform community detection thresholds.
- Developed Python scripts for processing CSV files, creating tag-pair views, and generating edge-related outputs with quantile-based thresholds.
- Explored the geography of knowledge through metrics analysis and provided recommendations for knowledge base optimization.
- Implemented refined data processing techniques, including anti-tautology guards and tag normalization, to improve data processing workflows.
- Addressed import and keyword argument errors in Python scripts, enhancing the functionality of the
kb_edapackage. - Evaluated and improved community alignment and edge scoring methods, providing actionable refinements.
Achievements
- Established parameter tiers for filtering edges in graph analysis.
- Successfully created various tag-pair subsets and enhanced data processing scripts.
- Improved the organization and navigation of the knowledge base through metric analysis.
- Resolved technical issues in Python scripts, leading to more robust data processing capabilities.
Pending Tasks
- Further alignment of tag vocabularies to prevent mismatches in community mapping.
- Continued development of CLI tools for chapter generation and community mapping.
- Ongoing refinement of data processing scripts to enhance community detection accuracy.