Information Package / Course Catalogue
Introduction to Graph Database Management and Mining
Course Code: CSE447
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: English
Work Placement: N/A
Theory: 2
Prt.: 2
Credit: 3
Lab: 0
ECTS: 6
Objectives of the Course

The objective of this course is to provide students with a solid foundation in graph data management and mining. Students will learn how to model complex data using graph structures and understand the theoretical principles behind graph representation, traversal, and querying. The course also covers key data mining techniques on graphs, including frequent subgraph mining, community detection and centrality analysis.

Course Content

This course covers the fundamentals of graph theory, graph data models and querying techniques. It includes topics such as graph traversal, graph similarity, frequent subgraph mining, community detection and centrality metrics. The course also introduces applications of graph mining in social network analysis, recommendation systems.

Name of Lecturer(s)
Learning Outcomes
1.Explains the fundamental concepts of graph theory and graph-based data representation.
2.Identifies key algorithms and techniques used in graph data querying and analysis.
3.Applies graph traversal, similarity, and pattern discovery methods to real-world datasets.
4.Analyzes network structures using frequent subgraph mining, community detection, and centrality metrics.
5.Evaluates graph-based approaches in domains such as social network analysis, recommendation, and anomaly detection.
6.Relates graph data management to broader data science and knowledge discovery tasks.
Recommended or Required Reading
1.Charu C. Aggarwal – Graph Data Mining: Algorithms and Applications (Springer, 2021)
2.Rajaraman, A., & Ullman, J. D. (2011). Mining of massive datasets. Autoedicion.
3.West, D. B. (2001). Introduction to graph theory (Vol. 2). Upper Saddle River: Prentice hall.
Weekly Detailed Course Contents
Week 1 - Theoretical & Practice
Introduction to Graphs and Applications
Week 2 - Theoretical & Practice
Graph Representations and Storage Models
Week 3 - Theoretical & Practice
Graph Theory Fundamentals
Week 4 - Theoretical & Practice
Graph Modeling for Complex Data
Week 5 - Theoretical & Practice
Graph Querying Concepts
Week 6 - Theoretical & Practice
Graph Similarity and Matching Techniques
Week 7 - Theoretical & Practice
Frequent Subgraph Mining: Foundations
Week 8 - Theoretical & Practice
Frequent Subgraph Mining: Algorithms and Applications
Week 9 - Theoretical & Practice
Community Detection in Graphs
Week 10 - Theoretical & Practice
Community Detection and Social Network Analysis
Week 11 - Theoretical & Practice
Centrality Metrics and Influence Modeling
Week 12 - Theoretical & Practice
Link Prediction and Graph Dynamics
Week 13 - Theoretical & Practice
Graph-Based Machine Learning
Week 14 - Theoretical & Practice
Real-World Applications and Research Trends
Assessment Methods and Criteria
Type of AssessmentCountPercent
Midterm Examination1%40
Final Examination1%60
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory142256
Lecture - Practice142256
Midterm Examination117219
Final Examination117219
TOTAL WORKLOAD (hours)150
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
OÇ-1
5
4
OÇ-2
5
OÇ-3
4
OÇ-4
4
4
OÇ-5
4
5
4
OÇ-6
Adnan Menderes University - Information Package / Course Catalogue
2026