42nd cycle

Network Science & Computational Social Science

Offered Courses

The PhD program offers a selection of advanced courses designed to expose candidates to cutting-edge research methodologies and emerging technological trends.

Medical Process Modeling and Management

The course will focus on medical processes, with a particular emphasis on their representation and management. Various representation models for clinical guidelines, clinical trials, and medical workflows will be examined, alongside key aspects concerning data acquisition and/or data mining, representation, and utilization (e.g., simulation and decision support). Finally, the treatment of patients with comorbidities will be considered as a prototype of a complex context where multiple models must be reconciled.

Students will acquire knowledge of the primary types of models for medical processes, as well as the operational tools to acquire, represent, and utilize them. The course will provide foundational knowledge on these topics, along with insights into advanced research and the most recent hot topics in the field.

Course Duration: 18 hours

Mathematics foundation of Networks

This course provides a rigorous exploration of the mathematical frameworks that govern the structure, behavior, and evolution of complex networks. Moving beyond purely empirical data analysis, students will delve into the underlying discrete mathematics, linear algebra, and probabilistic models that allow us to quantify and predict network phenomena. The course bridges traditional graph theory with modern network science, equipping researchers with the analytical tools needed to develop new models and algorithms for complex systems.

By the end of this course, doctoral candidates will have mastered the formal language of network topology, spectral graph theory, and random graph ensembles.

Course Duration: 24 hours

Network Science & Computational Social Science

This advanced course bridges the gap between data-driven computational methods and social theory. Students will explore how network science can decipher complex social systems, tracking everything from information diffusion and pandemic spread to political polarization and algorithmic bias. The course balances theoretical mathematical foundations with hands-on computational workflows, introducing students to state-of-the-art research trends in the field.

This course aims to equip doctoral candidates with the theoretical foundations and practical competencies necessary to analyze and model complex human behaviors through the lens of network science and computational social science.

Course Duration: 24 hours

Assessments & Final Exams

To obtain the course credits, PhD candidates will be required to choose one of the following options:

Research Project: Apply the methods learned in class to run a novel experiment, or to formulate a rigorous proof , producing a short workshop-style paper (4–6 pages).
Literature Review & Seminar: Deliver a 20-minute presentation analyzing two highly-cited recent papers on a controversial or breakthrough related topic.

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