Graph Theory

Content

Objective

-The aim of this course is to introduce the fundamental concepts of graph theory and modern network analysis methods, and to enable students to analyze complex real-world network data. Throughout the course, different types of networks such as social networks, information networks, and geographic networks will be studied through modeling, visualization, and interpretation techniques. In addition, students will gain an introductory understanding of Graph Neural Networks (GNNs) as a modern machine learning approach for graph-structured data.

Course Content

-This course covers the fundamental concepts of graph theory and basic graph algorithms. Students will work with graph analysis using the Python-based library NetworkX. Visualization techniques for networks will be introduced through the software Gephi, and several visualization projects will be carried out on real datasets. During the course, complex network examples such as Wikipedia networks and OpenStreetMap networks will be analyzed. Topics including centrality measures, link prediction methods, and community detection approaches will also be discussed. Finally, the course provides an introduction to Graph Neural Networks (GNNs) and graph-based machine learning methods.

Course Learning Outcomes

Upon successful completion of this course, students will be able to:


Upon successful completion of this course, students will be able to:

Explain the fundamental concepts of graph theory and basic graph algorithms.
Model real-world systems using graph structures.
Perform basic network analysis using NetworkX.
Visualize and interpret network data using tools such as Gephi.
Apply centrality measures and community detection methods in network analysis.
Explain the basic principles of link prediction problems.
Analyze complex networks and interpret the obtained results.
Demonstrate introductory-level knowledge of Graph Neural Networks (GNNs).

Weekly Contents