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Social Network Analysis in Practice (Wintersemester 2018/2019)

Dozent: Dr. Ralf Krestel (Information Systems) , Tim Repke (Information Systems)
Website zum Kurs: https://hpi.de/naumann/teaching/teaching/ws-1819/social-network-analysis-in-practice-ps-master.html

Allgemeine Information

  • Semesterwochenstunden: 4
  • ECTS: 6
  • Benotet: Ja
  • Einschreibefrist: 26.10.2018
  • Lehrform: Projektseminar
  • Belegungsart: Wahlpflichtmodul
  • Lehrsprache: Deutsch
  • Maximale Teilnehmerzahl: 12

Studiengänge & Module

IT-Systems Engineering MA
Data Engineering MA
  • DATA-Konzepte und Methoden
  • DATA-Techniken und Werkzeuge
  • DATA-Spezialisierung
  • PREP-Konzepte und Methoden
  • PREP-Techniken und Werkzeuge
  • PREP-Spezialisierung

Beschreibung

Note: Please refer to the course website linked above for more recent and complete information!

In this seminar, students will learn about methods from research areas of Text Mining and Graph Analysis and propose approaches to combine both areas to  analyse semi-structured data. Students will put their proposals into practice using real world social network data in the form of corporate emails: Every day, companies accumulate large amounts of heterogeneous data in the form of emails, documents like contracts and letters, or others. From text documents and their metadata, graphs can be extracted, where the nodes and edges are enriched with additional information. 
Inherent structures extracted from such heterogeneous graphs can support the work of journalists, auditors, or special investigators. The automated analysis should help them to uncover collaborations or simply support their work with massive amounts of data and figuring out "who knew what when" or who contributed in which capacity in a certain matter.

In the first phase of the seminar, students will get familiar with Text Mining and Social Network Analysis during a series of presentations.
They will work in small groups (or individually) on proposals that combine these two fields to 

  • classify components of the graph (e.g. different types of people, emails, or attachments) , 
  • extract information (e.g. version history of collaborations on documents or communication patterns),
  • aggregate information (e.g. embedding data into concepts).

Voraussetzungen

See course website.

Literatur

See course website.

Lern- und Lehrformen

See course website.

Leistungserfassung

See course website.

Termine

See course website.

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