This repository presents a foundational exploration of graph theory applied to organizational structure analysis. Using the administrative hierarchy of Academics West as a case study, we construct a directed acyclic graph (DAG) to model reporting relationships within an educational institution. This "Hello, Graph World" beachhead project demonstrates core competencies in network construction, data transformation, and visualization using Python.
- Transform tabular organizational data into a graph-based representation
- Demonstrate node and edge construction using the NetworkX library
- Visualize hierarchical relationships through network diagrams
- Establish foundational skills for advanced social network analysis (SNA)
Organizational structures can be conceptualized as directed graphs where:
| Element | Graph Representation |
|---|---|
| Employees | Nodes (V) |
| Reporting Relationships | Directed Edges (E) |
| Hierarchy | Graph Depth |
This project employs a DiGraph (directed graph) model, where edge direction flows from supervisor → subordinate, capturing the asymmetric nature of organizational authority.
- Organization: Academics West Educational Institution
- Format: Excel spreadsheet (.xlsx)
- Structure: Two-column relational mapping (person → manager)
| Tool | Purpose |
|---|---|
| 🐍 Python 3.x | Core programming language |
| 📊 Pandas | Data ingestion and transformation |
| 🌐 NetworkX | Graph construction and analysis |
| 📈 Matplotlib | Network visualization |
| 📓 Google Colab | Interactive development environment |
The resulting network visualization displays 18 nodes representing staff members and 17 directed edges representing reporting relationships. The graph reveals a hierarchical structure with the Chancellor/CEO as the root node and multiple branching pathways through department heads.
https://colab.research.google.com/drive/1t40cxjNE3nOkfqmRB-2Uxo2L_Sp2DaHj?usp=sharing
pip install pandas networkx matplotlib openpyxl- Clone this repository
- Open
Netw18.ipynbin Google Colab or Jupyter - Upload
org_chart.xlsxwhen prompted - Run all cells sequentially
├── Netw18.ipynb # Primary Jupyter notebook
├── org_chart.xlsx # Organizational data source
├── screenshot.png # Output visualization
└── README.md # Project documentation
- Implement centrality measures (degree, betweenness, closeness)
- Compare organizational structure to canonical graphs (e.g., Krackhardt Kite)
- Conduct temporal analysis of organizational evolution
- Apply community detection algorithms
- Tsvetovat, M., & Kouznetsov, A. (2011). Social Network Analysis for Startups: Finding Connections on the Social Web. O'Reilly Media.
Candace | Graduate Student | Network Analysis Enthusiast
This project is licensed under the MIT License - see the LICENSE file for details.
🌟 "The structure of a network determines its function." 🌟