Social Network Analysis in Practice (Wintersemester 2018/2019)
Lecturer:
Prof. Dr. Ralf Krestel
(Information Systems)
,
Tim Repke
(Information Systems)
Course Website:
https://hpi.de/en/naumann/teaching/teaching/ws-1819/social-network-analysis-in-practice-ps-master.html
General Information
- Weekly Hours: 4
- Credits: 6
- Graded:
yes
- Enrolment Deadline: 26.10.2018
- Teaching Form: Project seminar
- Enrolment Type: Compulsory Elective Module
- Course Language: German
- Maximum number of participants: 12
Programs, Module Groups & Modules
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-K Konzepte und Methoden
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-S Spezialisierung
- OSIS: Operating Systems & Information Systems Technology
- HPI-OSIS-T Techniken und Werkzeuge
- DATA: Data Analytics
- HPI-DATA-K Konzepte und Methoden
- DATA: Data Analytics
- HPI-DATA-T Techniken und Werkzeuge
- DATA: Data Analytics
- HPI-DATA-S Spezialisierung
- PREP: Data Preparation
- HPI-PREP-K Konzepte und Methoden
- PREP: Data Preparation
- HPI-PREP-T Techniken und Werkzeuge
- PREP: Data Preparation
- HPI-PREP-S Spezialisierung
Description
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).
Requirements
See course website.
Literature
See course website.
Learning
See course website.
Examination
See course website.
Dates
See course website.
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