Welcome to the Graph Mining (4041) course repository.
This repo serves as a central hub for all course materials, datasets, project ideas, and practical sessions related to graph analysis and network science.
This course explores how graphs (networks) model complex systems — from social connections and citation networks to biological pathways and infrastructure systems.
Students will learn how to analyze, model, and mine graph-structured data using concepts from mathematics, computer science, and data mining.
Instructor: Dr.Zeinab Maleki
Institution: Isfahan University of Technology
Level: Undergraduate
Semester: [Fall 2025]
By the end of the course, students will be able to:
- Understand and compute key graph metrics (degree distribution, clustering, path length, etc.)
- Compare random graph models (Erdős–Rényi, Watts–Strogatz, Barabási–Albert, Kleinberg)
- Perform graph mining tasks such as community detection and link prediction
- Use real-world graph datasets for analysis and project development
- Critically interpret structural differences between network types
- Perform Clustering tasks on differnet types of graphs