Network Analytics


Social Networks shape many aspects of how people and organizations interact, take decisions, and ultimately perform. With the advent of Social Media (e.g., Facebook and Twitter) and with the increasing digitization of all forms of communication and business processes, Network Analytics has become a valued asset to better understand how different agents interact and how to best take advantage of the network structure to increase overall system performance. This course will cover the fundamentals of network science, the methods, theories, and the procedures for data collection and analysis in very large social networks. Covered topics include clustering, information diffusion, organizational design, viral marketing, social media and others. 

General characterization





Responsible teacher

Rodrigo Crisóstomo Pereira Belo


Weekly - Available soon

Total - Available soon

Teaching language





There is no main textbook for this course. I will be using mostly online references for the R part. I will also be following Kadushin (2011) for the network sessions, but I will use materials from other books as well. Most of the network concepts are also covered by Easley & Kleinberg (2010) and by Hanneman (2005), both freely available online. The later sessions will also be based on scientific papers.

Teaching method

Classes will be taught in a seminar-style format in which the conceptual material will be introduced and connected with real-world cases. Most classes will also have a practical hands-on component in which students will apply the learned concepts in a practical setting by following a script presented by the instructor in class. Students should bring their laptops with the required software installed. Please follow the software installation instructions posted on Moodle. Practical skills will also be learned by working on various assignments. 

Evaluation method

Individual assignments 50%

Group Assignment 40% 

Class attendance and discussion board participation 10% 

Subject matter

 - Basic Network Concepts

- Degree Distributions

- Clustering

- Homophily

- Models of Network Formation

- Network Externalities

- Bass Model, SIR and SIS models

- Threshold Models

- Regression in Networked Data

- Empirical Challenges

- Randomized Experiments in Networks

- Social Capital

- Games in Networks