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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. To analyze large graphs, such as web graphs or social networks, 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 graph neural networls and graph processing systems for static and dynamic graphs. Furthermore, we study streaming and in-memory graph partitioners. More information: https://docs.google.com/presentation/d/1SfAXY8Gz7QvOgVFrp6KbXM2SQV-Hg9xMSKaqtdSKH1s/edit?usp=sharing Preliminary meeting 02.02.2022 2pm via zoom Link to Zoom-Meeting: https://tum-conf.zoom.us/j/66462061075 Meeting-ID: 664 6206 1075 Code: 082348

Basic knowledge of distributed systems.

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%)