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

Lecturer (assistant)
Duration2 SWS
TermSommersemester 2023
Language of instructionEnglish
Position within curriculaSee TUMonline
DatesSee TUMonline

Admission information


Modulkatalog: IN2107


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 at a large scale. In this seminar we will study several large-scale graph neural networks and graph processing systems for different types of graphs such as static graphs, dynamic graphs, heterogeneous graphs, hypergraphs, etc. An important pre-processing step to optimize (distributed) graph processing is graph partitioning. We will study both streaming and in-memory graph partitioners. More information: Preliminary meeting 07.02.2023 4pm via zoom Meeting ID: 697 2609 6905 Passcode: 894181


Basic knowledge of distributed systems.

Teaching and learning methods

Modulkatalog: IN2107 - Presentations - Written report with figures (5-8 pages ACM proceedings style), to submit 2 weeks after the presentation


Grade is based on written report with figures (5-8 pages ACM proceedings style) (50%) and presentation (50%)