Alireza Abdollahpouri, Ph.D.

Fall  2023

Lectures: Tuesday  11-14

Instructor:  Dr. A. Abdollahpouri 

Email: abdollahpouri@gmail.com ,

Course Description

Graph representations of relational data have become an important foundation to address data science and machine learning tasks across the sciences. Graph mining and learning techniques help us to detect functional modules in biological networks and communities in social networks, to find missing links in social networks, or to address node-, link-, or graph-level classification tasks. This course equips students with techniques to address supervised and unsupervised learning tasks in data on complex networks. We show how statistical learning techniques can be used to infer cluster patterns or predict links, introduce methods to learn low-dimensional vector-space representations of graph-structured data, and discuss applications of deep learning to complex networks. The course combines a series of lectures – which introduce theoretical concepts in statistical learning, representation learning, or graph neural networks – with practice sessions that show how we can apply them in practical graph learning tasks

Textbooks

Lecture notes

Grading Policy

Assignments

Useful Links and Documents

• PyG- PyTorch Geometric
• Open Graph benchmark (OGB)
• Deep Graph Library (DGL)
• Networkx Reference Manual

Datasets

• Complex Networks Datasets
• Stanford Large Network Dataset Collection
• Pajek datasets
• Colorado Index of Complex Networks
• InfoVis data
• UCINET data
• Netwiki data
• Harvard dataverse
• Newman data
• Alon data
• Arenas data
• Pozo data
• Cx-Nets data