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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 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: https://docs.google.com/presentation/d/1EURpuZeRgaaE0EeDv4Jp1pQkt8YuCf-oyhRdtloLNUI/edit?usp=sharing Preliminary meeting 07.02.2023 4pm via zoom https://tum-conf.zoom.us/j/69726096905?pwd=T09ySktzY0hOVEtuM3ZEaElOZk1JUT09 Meeting ID: 697 2609 6905 Passcode: 894181

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%)