Sociology for sustainability and analysis of global processes

The course syllabus includes the following topics: I Part: by middle November The Network Analysis evolution: from the Sociometry of J. L. Moreno and the turning point of the Harvard School • J. L. Moreno’s Sociometry • The School of Manchester • The Massachusets Institute of Technology (MIT) • The Harvard’s School Network Analysis main concepts • Definitions and main concepts • Network-level concepts • Adjacency matrices and degree centrality • Introduction to the NETWORK procedure Centrality Measures • Definitions and main concepts • Eigenvector centrality • Betweenness and closeness centrality • Influence centrality (self-study) • Hub and authority centrality • PageRank centrality II Part: by December - the end of the Course Analysis of Subnetworks • Connected and biconnected components • Maximal cliques • Community detection • Paths, shortest paths, and cycles • Pattern matching Bipartite Networks • Introduction to bipartite networks • Network projection Network Optimization • Definition and main concepts • Linear assignment problem • Minimum spanning tree • Maximum spanning tree (self-study) • Traveling salesman problem • Minimum cost network flow (self-study) Network Optimization Using the OPTMODEL Procedure

Fundamentals of statistics, multivariate statistics

For Attendants Handouts and teaching material will be provided by the lecturer. It will be integrated by the materials provided by SAS. Per non frequentanti Theoretical part: Linton C. Freeman, Lo sviluppo dell’analisi delle reti sociali. Uno studio di sociologia della scienza, Franco Angeli Practical part: Handouts will be provided by the lecturer, and uploaded on the Moodle platform

Lessons attendance is strongly recommended. The students who cannot attend the Course are invited to follow the programme for not attendants The lecturer records the lessons to facilitate catching up on missed classes or to review more complex topics.

Students attending classes will take a practical exam in a laboratory session solving 5 exercises using SAS. Not attendants students will take an oral exam based on a text book indicated by the professor

Students will learn how to apply the theoretical concepts and the methodology to relational data-sets, using the software SAS. Each lesson will be accompanied by a practical SAS laboratory session, based on a set of exercises on social and economic issues that students will be called to solve. At the end of the Course, students will not only be able to apply the network techniques, but also to describe, to discuss, and to interpret the results got. Students are constantly involved in active class-work sessions. Indeed, the teaching method aims at encouraging all students, individually or in group, to analyse and critically comment/interpret the processes and the results of the exercises solved, in order to develop capacity of synthesis and evaluation with respect to the issues proposed by the lecturer. The working group and the presentation/discussion of the results of the class activities (comment and interpretation of the results of the exercises solved) contribute to both the development of communication skills and the acquisition of the specific scientific technical language of the discipline. Lessons are recorded and uploaded to the Moodle Platform in order to support the study of both attendants and not attendants students. The lecturer uses the Classroom platform for a more streamlined sharing of educational materials, exercises, and homework with attending students, and the Moodle platform for non-attending ones (mostly).