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

Lecturer (assistant)
Duration2 SWS
TermWintersemester 2022/23
Language of instructionEnglish
Position within curriculaSee TUMonline
DatesSee TUMonline

Admission information


Modulkatalog: IN2107


Preliminary meeting at 18.07.2022, 2 PM - 3 PM will be ONLINE: (Kenncode: 292499) 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: Preliminary meeting at 18.07.2022, 2 PM - 3 PM will be ONLINE: (Kenncode: 292499)


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


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