Kies de Nederlandse taal
Course module: 202100031
Big data & network analysis (R)
Course info
Course module202100031
Credits (ECTS)4
Course typeStudy Unit
Language of instructionEnglish
Contact persondr. M. Amir Haeri
dr. M. Amir Haeri
Contactperson for the course
dr. M. Amir Haeri
dr. M. Amir Haeri
dr. S.M. van den Berg
R. Marinescu-Muster
Academic year2021
Starting block
Application procedureYou apply via OSIRIS Student
Registration using OSIRISYes
At the end of the course, students will be able to… (between brackets the number of the corresponding intended learning qualification of the programme):
  1. analyze the characteristics of each network at the network level and user level to understand the capability of the network in information transmission and the importance of each user (2.1, 2.4, 3.4);
  2. find communities in specific social networks and know their importance in information propagation and disseminating new technologies (2.1,2.4, 3.2, 4.2);
  3. identify high-impact users according to content and network features (2.1, 2.4, 3.2, 4.2);
  4. predict how social networks evolve and predict future links (2.1,2.4, 3.2, 4.2);
  5. visualize and interpret the results of social network analysis (2.4, 2.5, 3.4, 3.5, 4.4).
Social networks are one of the most important sources of big data. Processing this amount of data yields valuable knowledge for businesses, governments and society. For analyzing social network data, we need to use specific approaches for processing networks. The big data & network analysis course covers the main methods and techniques in social network analysis and provides hands-on practice with these methods. The students will first get acquainted with the basic concepts of social networks and understand how these networks evolve. Then they will learn a variety of methods for analyzing these networks, such as computing the importance of nodes and paths, identifying communities, and predicting the future states of the network. Also, attention is given to introducing different information diffusion models on social networks and the approaches of finding influential users. They will use R to conduct various social network analyses, and they will get familiar with multiple network visualization techniques. Real data will be gathered from social networks and processed using R. At the end of this course, students will be able to design and execute network analysis projects including collecting data and performing a systematic analysis of network data. The acquired knowledge and insights will be used in the Project and furthermore tested through an individual written exam (5R1).

This study unit is part of the Communication science module The network society. Because the four courses, which are part of the module, are highly related to each other it is not possible to follow this study unit separately.

Module 5
Participating study
Bachelor Communication Science
Required materials
See the digital learning management system Canvas of the University of Twente.
Recommended materials
Instructional modes
Presence dutyYes

Presence dutyYes

5R1: Individual test with open questions

Kies de Nederlandse taal