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About

The Graph Machine Learning Research Group at the University of Kurdistan is dedicated to advancing the field of artificial intelligence through innovative research on graph-structured data and deep learning techniques. Our interdisciplinary team focuses on developing novel algorithms and models that leverage the complex relationships inherent in graph data to solve real-world problems across various domains such as social networks, bioinformatics, recommendation systems, and more. Committed to excellence in both theoretical foundations and practical applications, we strive to push the boundaries of machine learning by exploring new methodologies that improve accuracy, scalability, and interpretability.

Members

Supervisor Photo
Dr. Alireza Abdollahpouri
Supervisor
Research interests include graph neural networks, deep learning, and AI applications in social networks.

Ph.D Students

PhD Student 1
Shahla Havas
Ph.D Student
Improving the performance of recommender systems using hybrid methods based on graph neural networks
PhD Student 2
Sajad Bastami
Ph.D Student
Interested in graph-based semi-supervised learning and AI interpretability.
PhD Student 3
Mohammad Aliabadi
Ph.D Student
Specializes in graph convolutional networks and recommendation systems.
PhD Student 4
Zahra Khani
Ph.D Student
Multi view clustering using Graph neural networks.

Master Students

Master Student 1
Mehrshad Fazli
Master Student
Focus on graph data mining and network analysis.
Master Student 2
Vida Zarei
Master Student
Inhibiting hiv molecule using graph neural network.
Master Student 3
Elahe Sadeghian
Master Student
Recommender systems
Master Student 4
Hanie Basati
Master Student
Focused on temporal graph analysis and AI ethics.
Master Student 5
Kaveh Muhamadi
Master Student
Works on graph-based natural language processing.
Master Student 5
Amir Bashiri
Master Student
RL-Enhanced GNNs for Fake news Detection.
Master Student 5
Shole Sharifian
Master Student
Interested in AI fairness and graph data visualization
Master Student 5
Arasteh Karimi
Master Student
Motif-Based Graph Contrastive Learning for drug-target binding affinity prediction
Master Student 5
Roya Matin
Master Student
Graph neural networks

External

Researcher
Mansour Joya
Researcher
Senior Software Developer | Researcher in Recommender Systems & Machine Learning

Alumni

  • Farshid Ahmad: Investigating the academic progress or decline of high school students in Sanandaj using graph analysis
  • Samaneh Ghaderi: Modularity Improvement for Community Detection in Overlapping Social Networks
  • Zahra Ghaffaripour: Community Detection in Weighted Networks using a Multiobjective Genetic Algorithm
  • Nasim Isaeian: Machine Learning Based Thyroid Disease Prediction Result Improvement Using Extracted Graph Features
  • Shakila Jahanbin: A Political Optimizer Algorithm to Achieve P-fairness in Multi-Stakeholder Recommender Systems
  • Sara Karamveiseh: Improving a Multi-Stakeholder Recommender System Using Multi objective Evolutionary Algorithm based on Decomposition
  • Reza Mahmoudi: Link Prediction by Adversarial Deep Nonnegative Matrix Factorization
  • Tara Noori: Fair multi-stakeholder movie recommender system with hypergraph ranking
  • Smira Rafiee: Link prediction based on common neighbors degree penalization
  • Shadi Rahimi: Presenting a population-based multi-objective approach for detecting communities in complex networks
  • Sakar Omar Khdir: Interlayer Link Prediction in Multiplex Networks by Analyzing Matching Degree
  • Mastooreh Armion: Identifying influential nodes in complex networks using reverse link prediction
  • Chiman Salavati: Identifying the Most Influential Users in Social Networks for Viral Marketing with Minimum Cost
  • Marjan Ziaei: Investigating and Reducing Cascading Failures in weighted complex Networks
  • Fatemeh Zamani: A data placement strategy to reduce cost and data transmission delay in fog Infrastructure
  • Kaziwa Karim Majid: An Anti-Money Laundering Method Using Graph Machine Learning
  • Sahar Dogohari: Improving a hot product recommendation system using tripartite graph and link prediction
  • Mahtab Ahmadi: Community detection Based on content in social networks using user frequent pattern mining and label propagation

Publications

Our 6 selected publications sorted by most recent.

Scalable and robust big data clustering

Graph Machine Learning

Abdollahpouri A., Havas Sh, University of Kurdistan Publications, 1st Edition 2025

Graph Machine Learning is a specialized field of machine learning that leverages graph-structured data to model complex relationships between entities, enabling tasks such as node classification, link prediction, and graph clustering. By exploiting the interconnected nature of data represented as nodes and edges, it provides powerful tools for applications ranging from social network analysis to drug discovery

Courses

Complex Networks
Link to Course
Machine Learning with Graphs
Link to Course

Contact

Postal Address:

Rooms 220 & 222

Department of Computer Engineering

University of Kurdistan

Sanandaj

Iran

Mail: gml@uok.ac.ir