851-0252-15L  Network Analysis

Courses

Catalogue data

Abstract	Network science is a distinct domain of data science that is characterized by a specific kind of data being studied. While areas of application range from archaeology to zoology, we concern ourselves with social networks for the most part. Emphasis is placed on descriptive and analytic approaches rather than theorizing, modeling, or data collection.
Learning objective	Students will be able to identify and categorize research problems that call for network approaches while appreciating differences across application domains and contexts. They will master a suite of mathematical and computational tools, and know how to design or adapt suitable methods for analysis. In particular, they will be able to evaluate such methods in terms of appropriateness and efficiency.
Content	The following topics will be covered with an emphasis on structural and computational approaches and frequent reference to their suitability with respect to substantive theory: * Empirical Research and Network Data * Macro and Micro Structure * Centrality * Roles * Cohesion * Influence
Lecture notes	Slides and lecture notes are distributed via the associated course moodle.
Literature	* Hennig, Brandes, Pfeffer & Mergel (2012). Studying Social Networks. Campus-Verlag. * Borgatti, Everett & Johnson (2013). Analyzing Social Networks. Sage. * Robins (2015). Doing Social Network Research. Sage. * Menczer, Fortunato & Davis (2020). A First Course in Network Science. Cambridge University Press. * Brandes & Erlebach (2005). Network Analysis. Springer LNCS 3418. * Wasserman & Faust (1994). Social Network Analysis. Cambridge University Press. * Kadushin (2012). Understanding Social Networks. Oxford University Press. * Gërxhani, De Graaf & Raub (2023). Handbook of Sociological Science. Edward Elgar.
Competencies	Subject-specific Competencies Concepts and Theories fostered Techniques and Technologies assessed Method-specific Competencies Analytical Competencies assessed Decision-making fostered Problem-solving assessed Social Competencies Communication fostered Self-presentation and Social Influence fostered Sensitivity to Diversity fostered Personal Competencies Adaptability and Flexibility fostered Creative Thinking assessed Critical Thinking assessed Integrity and Work Ethics fostered Self-awareness and Self-reflection fostered

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits	3 credits
Examiners	U. Brandes
Type	graded semester performance
Language of examination	English
Repetition	Repetition possible without re-enrolling for the course unit.
Admission requirement	Basic knowledge of discrete mathematics

Learning materials

Groups

Restrictions

Offered in

Programme	Section	Type
Data Science Master	Interdisciplinary Electives	W
Doctorate Humanities, Social and Political Sciences	Subject Specialisation	W
Science in Perspective	Sociology	W
Science in Perspective	D-INFK	W
Science in Perspective	D-MATH	W