🎓 Academic Graph Builder

Visual modeling and visualization of academic entities (students, professors, courses, projects, and publications) using Python and NetworkX.

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📦 Dataset Structure

This synthetic dataset represents the core components of an academic environment:

• Students (name, faculty number, major, course year, academic status)
• Professors (name, position, major, courses taught)
• Courses (course code, credits)
• Research Projects (title, leader, participants)
• Publications (title, authors)

All entities are stored as nodes in a directed graph (DiGraph), and relationships are edges with labels such as Enrolled In, Teaches, Participates In, Leads, and Author.

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🛠️ Technologies Used

• Python 3.x
• NetworkX for graph creation and export
• Matplotlib for static visualization
• Export formats: .graphml, .gexf, .json

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🔄 Graph Construction Logic

The graph includes:

• 7 students with varying majors and academic levels
• 2 professors leading courses and projects
• 2 core university courses
• 2 research projects with multi-student participation
• 1 academic publication with student-professor co-authorship

Each edge encodes a directional relationship (e.g., Professor -> Course, Student -> Research Project).

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📈 Visualization

The script visualizes the graph using a spring layout and assigns node colors by type:

• 🟩 Students
• 🟧 Professors
• 🟦 Courses
• 🟪 Research Projects
• 🟨 Publications

Result: a clear, color-coded view of the academic ecosystem.

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📤 Export Options

The final graph is exported in 3 formats:

• academic_graph.graphml – for use in Gephi, yEd, Neo4j
• academic_graph.gexf – for large-scale graph platforms
• academic_graph.json – for D3.js or custom visualization

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📌 Topics

python networkx graph-visualization academic-graph education-data students professors courses research-projects graph-export

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📎 About

This project is a compact and educational example of using graphs to model real-world academic data. It is suitable for:

• 📚 Teaching graph theory
• 🎓 University demos
• 📊 Visual analysis tools
• 🧪 Data modeling experiments