The course aims at introducing students to an in-depth conceptual understanding and analytical tools of the new paradigm of Network Science with applications to Social Systems, Economics and Finance, Infrastructural and Territorial Networks, Biological and Ecological systems, highlighting its potential relevance to the present business challenges. Students are encouraged to apply theoretical notions in practical sessions in order to solve empirical problems through a hands-on approach both with expressive programming languages and intuitive visual tools. They are also encouraged to conceive new projects involving the Network Science paradigm and try to solve small real-world cases of interest.

Knowledge and understanding: knowledge of the real-world complex and networked systems, and their social and business impact. Understanding of the main paradigms of analysis and interpretation. Ability to apply knowledge: ability to analyze complex systems in real-world environments with the tools introduced and to extract synthetic knowledge to characterize their social impact and business impact. Autonomy of judgment: ability to extract 'objective' knowledge from the complexity of these paradigms, as a well-founded basis to be actionable for real-world projects. Communication skills: ability to present the results of the analyses both through visual artifacts and public presentation in the form of short pitches. Learning skills: ability to know how to orient oneself independently and creatively in complex systems-related problems, to understand their structure and endless possibilities, and to extract their potential to transform our society.

The large production of data from the Internet, the web, and from the various devices that pervade our lives, represents, beyond the obvious dangers to privacy, a great opportunity to investigate society, business dynamics, and the reality that surrounds us. These new data sources originate mainly from Social Networks and in general from Social Media, where most of the social debate takes place, but also from Open Data initiatives, and from data produced inside companies (think of CRM platforms, e-commerce websites, IoT devices in production lines.). Even if the emerging AI computational frameworks in most cases are able to tackle these big data challenges, in many relevant cases, where the complexity of the system overwhelms the predictive power of the current approaches, a ‘network’ perspective is the only way out. In the last two decades, a disruptive paradigm has been introduced by physicists, applied mathematicians, and computer scientists capable of disentangling the challenge posed by Complex Systems and moreover able to extract valuable information for social systems and business managers, that is the field of Network Science. Network Science that originates from the theory of graphs has a long mathematical tradition, but only recently it has been understood how this paradigm is useful for understanding the complex phenomena taking place in our world, from economics to ecology, from infrastructures to transportation systems, to get to the brain architecture and applications to the dynamics of opinions and marketing campaigns, up to the recent modeling for the study of the spread of epidemics. In this course, we will cover both the theoretical part of Network Science and all possible relevant applications, both in the Social Media field and in the business sector. Python and the main network libraries will be the elective technical tools, but we will also use desktop applications in order to effectively manipulate and render beautiful network representation. At the end of the course, students will be able to independently tackle a Complex Network data analysis, knowing how to choose the appropriate sources, mapping them onto appropriate network structures and coding efficient Python algorithms for all possible applications of interest.

“A First Course in Network Science” by Filippo Menczer, Santo Fortunato, and Clayton A. Davis - Cambridge University Press “Data Science & Complex Networks” by Guido Caldarelli and Alessandro Chessa - Oxford University Press “Complex Network Analysis in Python” by Dimitry Zinoviev - The Pragmatic Bookshelf “Network Science with Python and NetworkX. Quick Start Guide” by Edward L. Platt - Packt>

-Online/on-site lectures -Live exercises with students -Case studies with the direct involvement of students -Problem-based learning -Peer education

The student's knowledge will be assessed in two different ways: 1. A final project-oriented group assignments on a topic that will be proposed by the groups and have to be accepted by the teacher. 2. Two personal assignments in the form of a written homework, in which the students will have to write a mini-paper (for an overall length of around 1.000 words per paper) on a specific topic assigned to each student by the teacher. The aim of the project-oriented group assignment is to give the students a way to test their capability in understanding the added value that Network Science can bring to a specific project. Ideally, the students should be able to point out why the projects that they are working on for their group assignment can get added value from the proposed paradigm. The teacher will also value the capability of students of bringing their own knowledge and domain competence into the project, e.g. exploring the financial or the economic aspects deriving from the complex system approach of a Network Science methodology within the project. The project-oriented group assignment will count for 60% of the final grade. The aim of the personal assignment is to assess the capability of the student to explore in deeper detail a specific subject, starting from the knowledge received from the course. The form of a short mini-paper (approximately 1000 words) is chosen to stress the importance of expressing the concepts in a concise and understandable way (quality over quantity). The personal assignments will count for 40% of the final grade. Finally, at the end of each week there will be an exchange of views between the students and the teacher on the topics and tools introduced during the week, in the form of a class debate. The students will be asked to elaborate on a very short informal assignment in written or oral form, either before the class or immediately before the debate. These oral or written elaborations will be shared with the class as a primer for the debate. These intermediate assignments and the following debate will not be evaluated, as they are intended as a moment in which students can learn together via trial and error, through direct confrontation with the class and sharing of ideas.

For the personal assignment, the topic will be assigned by the teacher. For the group assignment, the groups will be formed by the students and the subjects will be decided by the groups with some guidance from the teacher. The teacher can accept or modify the subject of the group depending on its relevance to the topics treated throughout the course.