A graph is a versatile data structure that can represent complex data as relationships between objects. Real-world networks modeled by graphs occur in many applications. Their analysis is key to understanding the structure and dynamics of the modeled processes. This course focuses on the algorithmic foundations of the analysis of massive graphs with machine learning and data mining methods.A key learning objective is to master the full algorithm engineering cycle in the context of the lecture. This means to model a real-world problem as algorithmic task, to design algorithmic solution methods for the task, to be able to analyze and compare these solution methods on a theoretical and empirical level, to implement (selected) algorithms, and to design and evaluate systematic experiments.Topics include representation learning, graph neural networks, graph clustering, link prediction, network motifs and others.Good knowledge of fundamental graph algorithms as well as linear algebra operations (as taught in Bachelor modules similar to "Algorithms and Data Structures" and "Linear Algebra and its Connections to Computer Science") is strongly recommended.
The course will be offered in English.
Die Veranstaltung wurde 2 mal im Vorlesungsverzeichnis SoSe 2024 gefunden:
