Prerequisites: Basic programming and algorithm skills
Audience: Data scientists, university students.
Duration: 3 days, 3 x6 = 18 hours
Graphs are powerful combinatorial data structures that can model real-life data and the relationships among the entities inside the data. These graph models and representations are commonly used in a variety of domains, including social networks, recommender systems, and biological systems. Some say “if you torture the data long enough, they will confess”. With graphs, you do not need to be cruel. If you know how to use them, they will tell you a lot. As a part of the EuroCC project, we, researchers from the NCC Turkey, have created a Graph School to make people learn more about graphs, why to use them, and how to use them. Here are the topics:
– Introduction to Graphs (Dr. Kamer Kaya)
– Network Science (Dr. Onur Varol)
– Spectral Graph Theory (Dr. Elif Vural)
– Graph Signal Processing (Dr. Elif Vural)
– Graph Embeddings (Dr. Öznur Taştan)
– Graph Databases (Dr. Pınar Karagöz)
– Graphs for NLP (Dr. Pınar Karagöz)
– Graph Neural Networks (Dr. Kamer Kaya)
– Graph Algorithms (Dr. Ezgi Karabulut Türkseven)
Pınar Karagöz is a Full Professor at Middle East Technical University, Computer Engineering Department. Her research is on data mining and data management, particularly graph, text, and streaming data processing and analytics.
Kamer Kaya is an Associate Professor at the Faculty of Engineering and Natural Sciences at Sabancı University. His research interests include Parallel Algorithms, Graph Algorithms, High-Performance Computing, and Cryptography. He is actively working on sparse computations on matrices, graphs, and tensors.
Öznur Taştan is an Assistant Professor at the Faculty of Engineering and Natural Sciences at Sabancı University. She actively works on machine learning and its applications in biology.
Onur Varol is an Assistant Professor at the Sabanci University Faculty of Engineering and Natural Sciences and Principal Investigator at the VIRAL Lab. His research focuses on developing techniques to analyze online behaviors to improve individual well-being and address societal problems using online data.
Elif Vural is an Associate Professor at Middle East Technical University, Department of Electrical and Electronics Engineering. Her research lies in the intersection of signal processing and machine learning and focuses on problems such as representation learning for data in graph domains, multiple modalities, and multiple domains.
Ezgi Karabulut Türkseven is an Assistant Professor at the Faculty of Engineering and Natural Sciences at Sabancı University. Her research interests are centered around discrete optimization and learning.