Social Network Analysis (SNA): Theory and Application

(in German: Social Network Analysis (SNA): Theory and Application - )

Module-ID: FIN-INF-110495

Link:	LSF
Responsibility:	Prof. Klaus Turowski
Lecturer:	Mariia Rizun
Classes:	Lecture/Exercise Social Network Analysis (SNA): Theory and Application
Applicability in curriculum:	- B.Sc. INF: Informatik - Wahlpflicht - B.Sc. CV: Informatik - Wahlpflicht - B.Sc. INGINF: Informatik - Wahlpflicht - B.Sc. WIF: Gestalten und Anwenden - Wahlpflicht

Abbreviation SNA-TaA

Credit Points 5

Semester Winter

Term 1.

Duration 1 Semester

Language english

Level Bachelor

Intended learning outcomes:
​ASPIRED TEACHING RESULTS

1. Comprehensive understanding of social network theory • Students will gain a deep understanding of the fundamental concepts and theories of SNA
2. Proficiency in R for SNA • Students will achieve a high level of competence in using R for network data analysis
3. Expertise in network visualization with Gephi • Students will be proficient in using Gephi to create visual representations of networks
4. Ability to conduct independent SNA projects • Students will be equipped to design and execute their own SNA projects, including data collection, analysis, and visualization
5. Critical evaluation of social networks • Students will develop the ability to critically assess social networks, understanding the implications of various network structures and metrics. They will be able to apply this knowledge to make informed decisions in research or professional practice
6. Application of SNA in diverse contexts • Students will be able to apply SNA techniques across a range of fields, such as sociology, business, public health, and online social media
7. Enhanced data-driven decision-making skills: • Students will develop strong data-driven decision-making skills. They will learn how to use SNA results to inform strategies, policies, and interventions in various professional contexts.
8. Communication skills • The course will enhance students' abilities to communicate complex analytical results effectively to both technical and non-technical audiences

Content:
CONTENT DESCRIPTION

1. Introduction to Social Network Analysis • Understanding that social network is not only Facebook or Instagram • History of the methodology development. First sociograms
2. Theoretical foundations of SNA • Key concepts: nodes, edges, actors, types of networks, graphs, etc. • Graph theory basics • Measures of centrality (degree, betweenness, closeness, eigenvector) • Network density, connectivity, and clustering
3. Data Collection and Preparation • Sources of network data • Data formats and preprocessing • Introduction to network data in R
4. SNA using R • Introduction to R and relevant packages • Creating and manipulating network objects Social Network Analysis (with the use of R and Gephi)

• Calculating network metrics • Visualizing networks with R • Community detection and modularity • Network dynamics and temporal networks 5. Network Visualization in Gephi • Overview of Gephi interface and functionalities • Importing network data into Gephi • Basic network visualization techniques • Customizing visualizations: layout algorithms, colors, and sizes • Creating and interpreting advanced visualizations • Exporting and sharing network visualizations 6. Applications of Social Network Analysis • Case studies in various fields (e.g., social media analysis, organizational networks, epidemiology) SOFTWARE: R and Gephi

Workload:
​50h lecture/exercise
100h independent work

Type of examination:	Teaching method / lecture hours per week (SWS):
home work	Lecture/Exercise (4 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
keine

Media:	Literature:

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