Building a comprehensive online library of various state-of-the-art algorithms in network science

The study of complex systems using networks has become a central theme across disciplines, from social sciences and economics to biology and information systems. Graph theory, which models entities as nodes and their pairwise relations as edges, has provided a powerful framework for analyzing such systems. Graph-based methods have enabled advances in tasks such as community detection, link prediction, and anomaly detection.

The main objective of the internship is to develop and expand a comprehensive online library that consolidates algorithms for mining graphs and hypergraphs, with the focus on synthetic graphs and hypergraphs. This platform will function as a central resource for researchers and practitioners, improving usability, reproducibility, and collaboration across the community.

Faculty Supervisor:

Pawel Pralat

Student:

Partner:

SGH Warsaw School of Economics

Discipline:

Mathematics

Sector:

Cyber Security; Information and Communications Technology; Technology

University:

Toronto Metropolitan University

Program: