Social Network Analysis

Credits: 
2
Hours: 
20
Area: 
Big Data Mining
Academic Year: 
2019-2020
2018-2019
2017-2018
2016-2017
Description: 

This course introduces students to the theories, concepts and measures of Social Network Analysis (SNA), that is aimed at characterizing the structure of large-scale Online Social Networks (OSNs). The course presents both classroom teaching to introduce theoretical concepts, and hands-on computer work to apply the theory on real large-scale datasets obtained from OSNs like Facebook and Twitter. The course aims to discuss in particular how the structural properties of social networks can be analyzed through SNA techniques, and how these properties can be used to characterize social phenomena arising in the society.

Notions: 

Representation of OSNs through graphs, Complex network indices for graph analysis, Weighted and unweighted graphs, Structural analysis of OSNs, Evolutionary properties of OSNs.

Technics and tools: 

Software tools for representing and analyzing large OSN graphs, Indices for the analysis of OSN graphs, Techniques for representing and analyzing the structure of personal social networks

Case studies and datasets: 

During the course, publicly available datasets containing traces of social interactions between users in OSNs like Facebook and Twitter will be used. Students will be provided with the needed software tools for the analysis of these datasets. Then, fundamental concepts and theories will be applied to these datasets. The datasets used in the course are the following:
- http://snap.stanford.edu/data/index.html
- http://socialnetworks.mpi-sws.org/datasets.html
- http://current.cs.ucsb.edu/facebook/

Competences: 

By the end of the course, students will develop skills to:
- Analyze of large-scale OSNs
- Work with the major indices for the analysis of the structure of OSNs
- Develop a critical awareness of the major software tools for the analysis of OSN graphs

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