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Course 2017-2018 a.y.

30389 - SOCIAL NETWORK ANALYSIS (Introduction to Network Science)


CLEAM - CLEF - CLEACC - BESS-CLES - WBB - BIEF - BIEM - BIG
Department of Management and Technology

Course taught in English


Go to class group/s: 31

CLEAM (6 credits - II sem. - OP  |  SECS-P/10) - CLEF (6 credits - II sem. - OP  |  SECS-P/10) - CLEACC (6 credits - II sem. - OP  |  SECS-P/10) - BESS-CLES (6 credits - II sem. - OP  |  SECS-P/10) - WBB (6 credits - II sem. - OP  |  SECS-P/10) - BIEF (6 credits - II sem. - OP  |  SECS-P/10) - BIEM (6 credits - II sem. - OP  |  SECS-P/10) - BIG (6 credits - II sem. - OP  |  SECS-P/10)
Course Director:
GIUSEPPE SODA

Classes: 31 (II sem.)
Instructors:
Class 31: GIUSEPPE SODA


Course Objectives
This course provides an intensive introduction to the field of social network analysis. The course is divided in class-lecture sessions and lab-exercise sessions. The objective is to familiarize students with the theory, research, methodological issues, and practical implications connected with social network analysis. Upon completion of the course, students should have a good grasp of social network concepts and methods, and be able to use them. The course covers theory, concept and method as well as hands-on application in the lab-sessions. The purpose of the lab-sessions is to teach how to analyze social network data. This means mastering the software tools as well as analytical strategies. Students need to bring their own laptop to effectively participate in the lab. Network concepts covered include graph-theoretic fundamentals, centrality, cohesion, subgroups, equivalence. Theoretical areas include embeddedness, social capital, organizational learning and organizational governance. This perspective is integrated with a practitioner perspective by using examples from consulting engagements.

Course Content Summary
PART I.
Introduction to network science; Network structures: understanding social networks; Using graphs to represent social relations; Creating network data; Visualizing networks; Using matrices to represent social relations; Working with network data.
PART II.
Network characteristics: Connection; Embedding; Ego networks; Centrality and power; Cliques and sub-groups; Positions and roles: The idea of equivalence; Multiplexity; Two-mode networks; Big networks; Small world networks; Scale free networks. Network origins; Network consequences; Network processes: Contagion; Diffusion; Influence; Network dynamics.
PART III.
Applications of Social Network Analysis
: intra-organizational communities and firms; strategic alliances and mergers & acquisitions.

Detailed Description of Assessment Methods
For attending students
  • Class contribution (exercises, class discussion, and, of course, attendance and punctuality): 40%.
  • Final written exam (test on the use of network concepts, data, and their applications): 60%.
For non attending students
  • Final written exam (test on the use of network concepts, data, and their applications): 100%.


Textbooks
  • S. WASSERMAN, K. FAUST, Social network analysis: Methods and applications (Vol. 8), Cambridge University Press.
  • R.A. HANNEMAN, M. RIDDLE, Introduction to social network methods, available on line at http://faculty.ucr.edu/~hanneman/nettext/
  • G. SODA (editor), Introduction to Network Science, Il Pellicano, EGEA, 2014.

Prerequisites
Fluent in English.
The teaching method requires the use of personal computers for the lab-sessions. The completion of exercises is an important aspect of the class, and help to familiarize with the UCINET software that includes the standard tools used in organizational network analysis.
All students are required to download on their PC the latest version of UCINET (www.analytictech.com).
Last change 13/06/2017 11:08