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Algorithms for Large-Scale Graph Processing
Last Updated: 2026-02-05 15:55:01
Abstract
This is a theory seminar, where we present and discuss recent algorithmic developments forprocessing large-scale graphs. In particular, we focus on Massively Parallel Computation (MPC)algorithms. MPC is a clean and general theoretical framework that captures the essential aspectsof computational problems in large-scale processing settings such as MapReduce, Hadoop,Spark, Dryad, etc.
Objective
This seminar familiarizes students with foundational aspects of large-scale graph processing, and especially the related algorithmic tools and techniques. In particular, we discuss recent developments in the area of Massively Parallel Computation. This is a mathematical abstraction of practical large-scale processing settings such as MapReduce, and it has been receiving significant attention over the past few years. The seminar assumes no particular familiarity with parallel computation. However, we expect that all the students are comfortable with basics of algorithms design and analysis, as well as probability theory. In the course of the seminar, the students learn how to structure a scientific presentation (in English) which covers the key ideas of a paper, while omitting the less significant details.
Content
The seminar will cover a number of the recent papers on Massively Parallel Computation. As mentioned above, no familiarity with parallel computation is needed and all the relevant background information will be explain by the instructor in the first lecture.
Resources
Literature
The papers will be presented in the first session of the seminar.
Learning Materials (Links)
- Main link
- Information
General Information
- Language
- English
- Levels
- DR , MSC , WBZ
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Max Places
- 12
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| seminar | Algorithms for Large-Scale Graph Processing |
|
2 h weekly |
Offered In
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Doctoral Department of Computer Science (More Information at: )