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Technische Universität München

Advanced Seminar Large-Scale Graph Processing and Graph Partitioning (IN2107, IN4435)

Lecturer (assistant)
Nikolai Merkel
Number 0000086868
Type seminar
Duration 2 SWS
Term Wintersemester 2021/22
Language of instruction English
Position within curricula See TUMonline
Dates See TUMonline

Dates

Admission information

See TUMonline

Objectives

Modulkatalog: IN2107

Description

Graphs are a fundamental data structure and are commonly used to model relationships between data points, e.g., links between web pages, friendships between users in a social network, etc. In the past decade, a large number of specialized distributed systems have emerged that are optimized for managing and processing graph-structured data. To analyze large graphs, such as web graphs or social networks, distributed graph processing systems are used, where a number of compute nodes execute a graph processing algorithm in a distributed fashion in parallel on different partitions of the graph. As a preprocessing step, the graph must be partitioned into several disjoint parts that are distributed across the compute nodes.

In this seminar we will study several large-scale (distributed) graph processing systems for static and dynamic graphs and graph neural networks. Furthermore, we study streaming and in-memory graph partitioners.

More information: https://docs.google.com/presentation/d/18tz8mf1JIuoFPLeeLLcDZ9S_AR3n742pSBcI6mMRD3Y/edit?usp=sharing

Preliminary meeting at 09.07.2021 2pm via zoom
Link: https://tum-conf.zoom.us/j/61241702728
Meeting-ID: 612 4170 2728
Code: 430026

Prerequisites

Basic knowledge of distributed systems.

Teaching and learning methods

Modulkatalog: IN2107
- Presentations
- Written report with figures (ACM proceedings style), to submit 2 weeks after the presentation

Examination

Grade is based on written report with figures (ACM proceedings style) (50%) and presentation (50%)

Links

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